Multi-agent machine learning a reinforcement approach

CERN Document Server

Schwartz, H M

2014-01-01

The book begins with a chapter on traditional methods of supervised learning, covering recursive least squares learning, mean square error methods, and stochastic approximation. Chapter 2 covers single agent reinforcement learning. Topics include learning value functions, Markov games, and TD learning with eligibility traces. Chapter 3 discusses two player games including two player matrix games with both pure and mixed strategies. Numerous algorithms and examples are presented. Chapter 4 covers learning in multi-player games, stochastic games, and Markov games, focusing on learning multi-pla

What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated.

Science.gov (United States)

Kumaran, Dharshan; Hassabis, Demis; McClelland, James L

2016-07-01

We update complementary learning systems (CLS) theory, which holds that intelligent agents must possess two learning systems, instantiated in mammalians in neocortex and hippocampus. The first gradually acquires structured knowledge representations while the second quickly learns the specifics of individual experiences. We broaden the role of replay of hippocampal memories in the theory, noting that replay allows goal-dependent weighting of experience statistics. We also address recent challenges to the theory and extend it by showing that recurrent activation of hippocampal traces can support some forms of generalization and that neocortical learning can be rapid for information that is consistent with known structure. Finally, we note the relevance of the theory to the design of artificial intelligent agents, highlighting connections between neuroscience and machine learning. Copyright © 2016 Elsevier Ltd. All rights reserved.

Nash Equilibrium of Social-Learning Agents in a Restless Multiarmed Bandit Game.

Science.gov (United States)

Nakayama, Kazuaki; Hisakado, Masato; Mori, Shintaro

2017-05-16

We study a simple model for social-learning agents in a restless multiarmed bandit (rMAB). The bandit has one good arm that changes to a bad one with a certain probability. Each agent stochastically selects one of the two methods, random search (individual learning) or copying information from other agents (social learning), using which he/she seeks the good arm. Fitness of an agent is the probability to know the good arm in the steady state of the agent system. In this model, we explicitly construct the unique Nash equilibrium state and show that the corresponding strategy for each agent is an evolutionarily stable strategy (ESS) in the sense of Thomas. It is shown that the fitness of an agent with ESS is superior to that of an asocial learner when the success probability of social learning is greater than a threshold determined from the probability of success of individual learning, the probability of change of state of the rMAB, and the number of agents. The ESS Nash equilibrium is a solution to Rogers' paradox.

Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction

OpenAIRE

Lopes, Manuel; Montesano, Luis

2014-01-01

In this survey we present different approaches that allow an intelligent agent to explore autonomous its environment to gather information and learn multiple tasks. Different communities proposed different solutions, that are in many cases, similar and/or complementary. These solutions include active learning, exploration/exploitation, online-learning and social learning. The common aspect of all these approaches is that it is the agent to selects and decides what information to gather next. ...

Pedagogical Agents as Learning Companions: The Impact of Agent Emotion and Gender

Science.gov (United States)

Kim, Yanghee; Baylor, A. L.; Shen, E.

2007-01-01

The potential of emotional interaction between human and computer has recently interested researchers in human-computer interaction. The instructional impact of this interaction in learning environments has not been established, however. This study examined the impact of emotion and gender of a pedagogical agent as a learning companion (PAL) on…

Construction of a Learning Agent Handling Its Rewards According to Environmental Situations

Science.gov (United States)

Moriyama, Koichi; Numao, Masayuki

The authors aim at constructing an agent which learns appropriate actions in a Multi-Agent environment with and without social dilemmas. For this aim, the agent must have nonrationality that makes it give up its own profit when it should do that. Since there are many studies on rational learning that brings more and more profit, it is desirable to utilize them for constructing the agent. Therefore, we use a reward-handling manner that makes internal evaluation from the agent's rewards, and then the agent learns actions by a rational learning method with the internal evaluation. If the agent has only a fixed manner, however, it does not act well in the environment with and without dilemmas. Thus, the authors equip the agent with several reward-handling manners and criteria for selecting an effective one for the environmental situation. In the case of humans, what generates the internal evaluation is usually called emotion. Hence, this study also aims at throwing light on emotional activities of humans from a constructive view. In this paper, we divide a Multi-Agent environment into three situations and construct an agent having the reward-handling manners and the criteria. We observe that the agent acts well in all the three Multi-Agent situations composed of homogeneous agents.

Sample efficient multiagent learning in the presence of Markovian agents

CERN Document Server

Chakraborty, Doran

2014-01-01

The problem of Multiagent Learning (or MAL) is concerned with the study of how intelligent entities can learn and adapt in the presence of other such entities that are simultaneously adapting. The problem is often studied in the stylized settings provided by repeated matrix games (a.k.a. normal form games). The goal of this book is to develop MAL algorithms for such a setting that achieve a new set of objectives which have not been previously achieved. In particular this book deals with learning in the presence of a new class of agent behavior that has not been studied or modeled before in a MAL context: Markovian agent behavior. Several new challenges arise when interacting with this particular class of agents. The book takes a series of steps towards building completely autonomous learning algorithms that maximize utility while interacting with such agents. Each algorithm is meticulously specified with a thorough formal treatment that elucidates its key theoretical properties.

Courseware Development with Animated Pedagogical Agents in Learning System to Improve Learning Motivation

Science.gov (United States)

Chin, Kai-Yi; Hong, Zeng-Wei; Huang, Yueh-Min; Shen, Wei-Wei; Lin, Jim-Min

2016-01-01

The addition of animated pedagogical agents (APAs) in computer-assisted learning (CAL) systems could successfully enhance students' learning motivation and engagement in learning activities. Conventionally, the APA incorporated multimedia materials are constructed through the cooperation of teachers and software programmers. However, the thinking…

Semi-Cooperative Learning in Smart Grid Agents

Science.gov (United States)

2013-12-01

this PhD program , but watching you grow has only made me realize how much more awesome human learning is. You have been a source of profound joy and...which should alleviate concern for scala - bility along this dimension. • Learning the negotiation model: Figure 6.23 shows single-episode results that...for Semi-cooperative Multi-agent Coordination. In IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning . [Prendergast, 1999

Learning Networks: connecting people, organizations, autonomous agents and learning resources to establish the emergence of effective lifelong learning

NARCIS (Netherlands)

Koper, Rob; Sloep, Peter

2003-01-01

Koper, E.J.R., Sloep, P.B. (2002) Learning Networks connecting people, organizations, autonomous agents and learning resources to establish the emergence of effective lifelong learning. RTD Programma into Learning Technologies 2003-2008. More is different… Heerlen, Nederland: Open Universiteit

Fast Conflict Resolution Based on Reinforcement Learning in Multi-agent System

Institute of Scientific and Technical Information of China (English)

PIAOSonghao; HONGBingrong; CHUHaitao

2004-01-01

In multi-agent system where each agen thas a different goal (even the team of agents has the same goal), agents must be able to resolve conflicts arising in the process of achieving their goal. Many researchers presented methods for conflict resolution, e.g., Reinforcement learning (RL), but the conventional RL requires a large computation cost because every agent must learn, at the same time the overlap of actions selected by each agent results in local conflict. Therefore in this paper, we propose a novel method to solve these problems. In order to deal with the conflict within the multi-agent system, the concept of potential field function based Action selection priority level (ASPL) is brought forward. In this method, all kinds of environment factor that may have influence on the priority are effectively computed with the potential field function. So the priority to access the local resource can be decided rapidly. By avoiding the complex coordination mechanism used in general multi-agent system, the conflict in multi-agent system is settled more efficiently. Our system consists of RL with ASPL module and generalized rules module. Using ASPL, RL module chooses a proper cooperative behavior, and generalized rule module can accelerate the learning process. By applying the proposed method to Robot Soccer, the learning process can be accelerated. The results of simulation and real experiments indicate the effectiveness of the method.

Online Bahavior Aquisition of an Agent based on Coaching as Learning Assistance

Science.gov (United States)

Hirokawa, Masakazu; Suzuki, Kenji

This paper describes a novel methodology, namely ``Coaching'', which allows humans to give a subjective evaluation to an agent in an iterative manner. This is an interactive learning method to improve the reinforcement learning by modifying a reward function dynamically according to given evaluations by a trainer and the learning situation of the agent. We demonstrate that the agent can learn different reward functions by given instructions such as ``good or bad'' by human's observation, and can also obtain a set of behavior based on the learnt reward functions through several experiments.

An Active Learning Exercise for Introducing Agent-Based Modeling

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Pinder, Jonathan P.

2013-01-01

Recent developments in agent-based modeling as a method of systems analysis and optimization indicate that students in business analytics need an introduction to the terminology, concepts, and framework of agent-based modeling. This article presents an active learning exercise for MBA students in business analytics that demonstrates agent-based…

An embodiment effect in computer-based learning with animated pedagogical agents.

Science.gov (United States)

Mayer, Richard E; DaPra, C Scott

2012-09-01

How do social cues such as gesturing, facial expression, eye gaze, and human-like movement affect multimedia learning with onscreen agents? To help address this question, students were asked to twice view a 4-min narrated presentation on how solar cells work in which the screen showed an animated pedagogical agent standing to the left of 11 successive slides. Across three experiments, learners performed better on a transfer test when a human-voiced agent displayed human-like gestures, facial expression, eye gaze, and body movement than when the agent did not, yielding an embodiment effect. In Experiment 2 the embodiment effect was found when the agent spoke in a human voice but not in a machine voice. In Experiment 3, the embodiment effect was found both when students were told the onscreen agent was consistent with their choice of agent characteristics and when inconsistent. Students who viewed a highly embodied agent also rated the social attributes of the agent more positively than did students who viewed a nongesturing agent. The results are explained by social agency theory, in which social cues in a multimedia message prime a feeling of social partnership in the learner, which leads to deeper cognitive processing during learning, and results in a more meaningful learning outcome as reflected in transfer test performance.

Emotion in reinforcement learning agents and robots: A survey

OpenAIRE

Moerland, T.M.; Broekens, D.J.; Jonker, C.M.

2018-01-01

This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computational emotion models are usually grounded in the agent's decision making architecture, of which RL is an important subclass. Studying emotions in RL-based agents is useful for ...

Pedagogical Agents as Learning Companions: The Role of Agent Competency and Type of Interaction

Science.gov (United States)

Kim, Yanghee; Baylor, Amy L.

2006-01-01

This study was designed to examine the effects of the competency (low vs. high) and interaction type (proactive vs. responsive) of pedagogical agents as learning companions (PALs) on learning, self-efficacy, and attitudes. Participants were 72 undergraduates in an introductory computer-literacy course who were randomly assigned to one of four…

OWL model of multi-agent Smart-system of distance learning for people with vision disabilities

Directory of Open Access Journals (Sweden)

Galina A. Samigulina

2017-01-01

Full Text Available The aim of the study is to develop an ontological model of multiagent smart-system of distance learning for visually impaired people based on Java Agent Development Framework for obtaining high-quality engineering education in laboratories of join use on modern equipment.Materials and methods of research. In developing multi-agent smart-system of distance learning, using various agents based on cognitive, ontological, statistical and intellectual methods is important. It is more convenient to implement this task in the form of software using multi-agent approach and Java Agent Development Framework. The main advantages of the platform are stability of operation, clear interface, simplicity of creating agents and extensive user database. In multi-agent systems, the solution is obtained automatically as result of interaction of many independent, purposeful agents. Each agent can perform certain tasks and pursue specified goals. Intellectual multi-agent systems and practical applications in distance learning based on them are considered.Results. The structural diagram of functioning of smart system distance learning for visually impaired people using various agents based on the system approach and the multi-agent platform Java Agent Development Framework is developed. The complex approach of distance learning of visually impaired people for obtaining highquality engineering education in laboratories of joint use on modern equipment is offered.The ontological model of multi-agent smart-system with a detailed description of the functions of following agents is created: personal, manager, ontological, cognitive, statistical, intellectual, shared laboratory agent, health agent, assistant to the agent and state agent. These agents execute their individual functions and provide a quality environment for learning.Conclusion. Thus, the proposed smart-system of distance learning for visually impaired people can significantly improve effectiveness and

Emotion in reinforcement learning agents and robots : A survey

NARCIS (Netherlands)

Moerland, T.M.; Broekens, D.J.; Jonker, C.M.

2018-01-01

This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action

Coordinating Decentralized Learning and Conflict Resolution across Agent Boundaries

Science.gov (United States)

Cheng, Shanjun

2012-01-01

It is crucial for embedded systems to adapt to the dynamics of open environments. This adaptation process becomes especially challenging in the context of multiagent systems because of scalability, partial information accessibility and complex interaction of agents. It is a challenge for agents to learn good policies, when they need to plan and…

Personalised learning object based on multi-agent model and learners’ learning styles

Directory of Open Access Journals (Sweden)

Noppamas Pukkhem

2011-09-01

Full Text Available A multi-agent model is proposed in which learning styles and a word analysis technique to create a learning object recommendation system are used. On the basis of a learning style-based design, a concept map combination model is proposed to filter out unsuitable learning concepts from a given course. Our learner model classifies learners into eight styles and implements compatible computational methods consisting of three recommendations: i non-personalised, ii preferred feature-based, and iii neighbour-based collaborative filtering. The analysis of preference error (PE was performed by comparing the actual preferred learning object with the predicted one. In our experiments, the feature-based recommendation algorithm has the fewest PE.

Optimal control in microgrid using multi-agent reinforcement learning.

Science.gov (United States)

Li, Fu-Dong; Wu, Min; He, Yong; Chen, Xin

2012-11-01

This paper presents an improved reinforcement learning method to minimize electricity costs on the premise of satisfying the power balance and generation limit of units in a microgrid with grid-connected mode. Firstly, the microgrid control requirements are analyzed and the objective function of optimal control for microgrid is proposed. Then, a state variable "Average Electricity Price Trend" which is used to express the most possible transitions of the system is developed so as to reduce the complexity and randomicity of the microgrid, and a multi-agent architecture including agents, state variables, action variables and reward function is formulated. Furthermore, dynamic hierarchical reinforcement learning, based on change rate of key state variable, is established to carry out optimal policy exploration. The analysis shows that the proposed method is beneficial to handle the problem of "curse of dimensionality" and speed up learning in the unknown large-scale world. Finally, the simulation results under JADE (Java Agent Development Framework) demonstrate the validity of the presented method in optimal control for a microgrid with grid-connected mode. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

An agent-based approach equipped with game theory. Strategic collaboration among learning agents during a dynamic market change in the California electricity crisis

Energy Technology Data Exchange (ETDEWEB)

Sueyoshi, Toshiyuki [Department of Management, New Mexico Institute of Mining and Technology, Socorro, NM 87801 (United States); Department of Industrial and Information Management, National Cheng Kung University, Tainan (China)

2010-09-15

An agent-based approach is a numerical (computer-intensive) method to explore the complex characteristics and dynamics of microeconomics. Using the agent-based approach, this study investigates the learning speed of traders and their strategic collaboration in a dynamic market change of electricity. An example of such a market change can be found in the California electricity crisis (2000-2001). This study incorporates the concept of partial reinforcement learning into trading agents and finds that they have two learning components: learning from a dynamic market change and learning from collaboration with other traders. The learning speed of traders becomes slow when a large fluctuation occurs in the power exchange market. The learning speed depends upon the type of traders, their learning capabilities and the fluctuation of market fundamentals. The degree of collaboration among traders gradually reduces during the electricity crisis. The strategic collaboration among traders is examined by a large simulator equipped with multiple learning capabilities. (author)

An agent-based approach equipped with game theory. Strategic collaboration among learning agents during a dynamic market change in the California electricity crisis

International Nuclear Information System (INIS)

Sueyoshi, Toshiyuki

2010-01-01

An agent-based approach is a numerical (computer-intensive) method to explore the complex characteristics and dynamics of microeconomics. Using the agent-based approach, this study investigates the learning speed of traders and their strategic collaboration in a dynamic market change of electricity. An example of such a market change can be found in the California electricity crisis (2000-2001). This study incorporates the concept of partial reinforcement learning into trading agents and finds that they have two learning components: learning from a dynamic market change and learning from collaboration with other traders. The learning speed of traders becomes slow when a large fluctuation occurs in the power exchange market. The learning speed depends upon the type of traders, their learning capabilities and the fluctuation of market fundamentals. The degree of collaboration among traders gradually reduces during the electricity crisis. The strategic collaboration among traders is examined by a large simulator equipped with multiple learning capabilities. (author)

Pedagogical Agents as Learning Companions: The Role of Agent Competency and Type of Interaction

OpenAIRE

Kim, Yanghee; Baylor, Amy L.; PALS Group,

2006-01-01

This study was designed to examine the effects of the competency (low vs. high) and interaction type (proactive vs. responsive) of pedagogical agents as learning companions (PALs) on learning, self-efficacy, and attitudes. Participants were 72 undergraduates in an introductory computer-literacy course who were randomly assigned to one of four treatments: Low-Proactive, Low-Responsive, High-Proactive, and High-Responsive. Results indicated a main effect for PAL competency. Students who worked ...

Situation Creator: A Pedagogical Agent Creating Learning Opportunities

NARCIS (Netherlands)

Miao, Yongwu; Hoppe, Ulrich; Pinkwart, Niels

2007-01-01

Miao, Y., Hoppe, H. U., & Pinkwart, N. (2007). Situation Creator: A Pedagogical Agent Creating Learning Opportunities. In R. Luckin, K. Koedinger & J. Greer (Eds.), Proceedings of the 13th International Conference on Artificial Intelligence in Education (pp. 614-617). Amsterdam, The Netherlands: IOS

Multi-agents and learning: Implications for Webusage mining

Science.gov (United States)

Lotfy, Hewayda M.S.; Khamis, Soheir M.S.; Aboghazalah, Maie M.

2015-01-01

Characterization of user activities is an important issue in the design and maintenance of websites. Server weblog files have abundant information about the user’s current interests. This information can be mined and analyzed therefore the administrators may be able to guide the users in their browsing activity so they may obtain relevant information in a shorter span of time to obtain user satisfaction. Web-based technology facilitates the creation of personally meaningful and socially useful knowledge through supportive interactions, communication and collaboration among educators, learners and information. This paper suggests a new methodology based on learning techniques for a Web-based Multiagent-based application to discover the hidden patterns in the user’s visited links. It presents a new approach that involves unsupervised, reinforcement learning, and cooperation between agents. It is utilized to discover patterns that represent the user’s profiles in a sample website into specific categories of materials using significance percentages. These profiles are used to make recommendations of interesting links and categories to the user. The experimental results of the approach showed successful user pattern recognition, and cooperative learning among agents to obtain user profiles. It indicates that combining different learning algorithms is capable of improving user satisfaction indicated by the percentage of precision, recall, the progressive category weight and F1-measure. PMID:26966569

Quantum-enhanced deliberation of learning agents using trapped ions

Science.gov (United States)

Dunjko, V.; Friis, N.; Briegel, H. J.

2015-02-01

A scheme that successfully employs quantum mechanics in the design of autonomous learning agents has recently been reported in the context of the projective simulation (PS) model for artificial intelligence. In that approach, the key feature of a PS agent, a specific type of memory which is explored via random walks, was shown to be amenable to quantization, allowing for a speed-up. In this work we propose an implementation of such classical and quantum agents in systems of trapped ions. We employ a generic construction by which the classical agents are ‘upgraded’ to their quantum counterparts by a nested process of adding coherent control, and we outline how this construction can be realized in ion traps. Our results provide a flexible modular architecture for the design of PS agents. Furthermore, we present numerical simulations of simple PS agents which analyze the robustness of our proposal under certain noise models.

Teachable Agents and the Protege Effect: Increasing the Effort towards Learning

Science.gov (United States)

Chase, Catherine C.; Chin, Doris B.; Oppezzo, Marily A.; Schwartz, Daniel L.

2009-01-01

Betty's Brain is a computer-based learning environment that capitalizes on the social aspects of learning. In Betty's Brain, students instruct a character called a Teachable Agent (TA) which can reason based on how it is taught. Two studies demonstrate the "protege effect": students make greater effort to learn for their TAs than they do…

Learning in engineered multi-agent systems

Science.gov (United States)

Menon, Anup

Consider the problem of maximizing the total power produced by a wind farm. Due to aerodynamic interactions between wind turbines, each turbine maximizing its individual power---as is the case in present-day wind farms---does not lead to optimal farm-level power capture. Further, there are no good models to capture the said aerodynamic interactions, rendering model based optimization techniques ineffective. Thus, model-free distributed algorithms are needed that help turbines adapt their power production on-line so as to maximize farm-level power capture. Motivated by such problems, the main focus of this dissertation is a distributed model-free optimization problem in the context of multi-agent systems. The set-up comprises of a fixed number of agents, each of which can pick an action and observe the value of its individual utility function. An individual's utility function may depend on the collective action taken by all agents. The exact functional form (or model) of the agent utility functions, however, are unknown; an agent can only measure the numeric value of its utility. The objective of the multi-agent system is to optimize the welfare function (i.e. sum of the individual utility functions). Such a collaborative task requires communications between agents and we allow for the possibility of such inter-agent communications. We also pay attention to the role played by the pattern of such information exchange on certain aspects of performance. We develop two algorithms to solve this problem. The first one, engineered Interactive Trial and Error Learning (eITEL) algorithm, is based on a line of work in the Learning in Games literature and applies when agent actions are drawn from finite sets. While in a model-free setting, we introduce a novel qualitative graph-theoretic framework to encode known directed interactions of the form "which agents' action affect which others' payoff" (interaction graph). We encode explicit inter-agent communications in a directed

Strategies to Enhance Online Learning Teams. Team Assessment and Diagnostics Instrument and Agent-based Modeling

Science.gov (United States)

2010-08-12

Strategies to Enhance Online Learning Teams Team Assessment and Diagnostics Instrument and Agent-based Modeling Tristan E. Johnson, Ph.D. Learning ...REPORT DATE AUG 2010 2. REPORT TYPE 3. DATES COVERED 00-00-2010 to 00-00-2010 4. TITLE AND SUBTITLE Strategies to Enhance Online Learning ...TeamsTeam Strategies to Enhance Online Learning Teams: Team Assessment and Diagnostics Instrument and Agent-based Modeling 5a. CONTRACT NUMBER 5b. GRANT

Quantum-enhanced deliberation of learning agents using trapped ions

International Nuclear Information System (INIS)

Dunjko, V; Friis, N; Briegel, H J

2015-01-01

A scheme that successfully employs quantum mechanics in the design of autonomous learning agents has recently been reported in the context of the projective simulation (PS) model for artificial intelligence. In that approach, the key feature of a PS agent, a specific type of memory which is explored via random walks, was shown to be amenable to quantization, allowing for a speed-up. In this work we propose an implementation of such classical and quantum agents in systems of trapped ions. We employ a generic construction by which the classical agents are ‘upgraded’ to their quantum counterparts by a nested process of adding coherent control, and we outline how this construction can be realized in ion traps. Our results provide a flexible modular architecture for the design of PS agents. Furthermore, we present numerical simulations of simple PS agents which analyze the robustness of our proposal under certain noise models. (paper)

Pedagogical Agents as Learning Companions: Building Social Relations with Learners

OpenAIRE

Kim, Yanghee

2005-01-01

This study examined the potential of pedagogical agents as learning companions (PALs) to build social relations with learners and, consequently, to motivate learning. The study investigated the impact of PAL affect (positive vs. negative vs. neutral), PAL gender (male vs. female), and learner gender (male vs. female) on learners’ social judgments, motivation, and learning in a controlled experiment. Participants were 142 college students in a computer-literacy course. Overall, the results ind...

Developing multimodal conversational agents for an enhanced e-learning experience

Directory of Open Access Journals (Sweden)

David GRIOL

2014-10-01

Full Text Available Conversational agents have become a strong alternative to enhance educational systems with intelligent communicative capabilities, provide motivation and engagement, and increment significant learning and helping in the acquisition of meta-cognitive skills. In this paper, we present Geranium, a multimodal conversational agent that helps children to appreciate and protect their environment. The system, which integrates an interactive chatbot, has been developed by means of a modular and scalable framework that eases building pedagogic conversational agents that can interact with the students using speech and natural language.

Smart Residential Buildings as Learning Agent Organizations in the Internet of Things

Directory of Open Access Journals (Sweden)

Schatten Markus

2014-03-01

Full Text Available Background: Smart buildings are one of the major application areas of technologies bound to embedded systems and the Internet of things. Such systems have to be adaptable and flexible in order to provide better services to its residents. Modelling such systems is an open research question. Herein, the question is approached using an organizational modelling methodology bound to the principles of the learning organization. Objectives: Providing a higher level of abstraction for understanding, developing and maintaining smart residential buildings in a more human understandable form. Methods/Approach: Organization theory provides us with the necessary concepts and methodology to approach complex organizational systems. Results: A set of principles for building learning agent organizations, a formalization of learning processes for agents, a framework for modelling knowledge transfer between agents and the environment, and a tailored organizational structure for smart residential buildings based on Nonaka’s hypertext organizational form. Conclusions: Organization theory is a promising field of research when dealing with complex engineering systems

Adventitious agents in viral vaccines: lessons learned from 4 case studies.

Science.gov (United States)

Petricciani, John; Sheets, Rebecca; Griffiths, Elwyn; Knezevic, Ivana

2014-09-01

Since the earliest days of biological product manufacture, there have been a number of instances where laboratory studies provided evidence for the presence of adventitious agents in a marketed product. Lessons learned from such events can be used to strengthen regulatory preparedness for the future. We have therefore selected four instances where an adventitious agent, or a signal suggesting the presence of an agent, was found in a viral vaccine, and have developed a case study for each. The four cases are: a) SV40 in polio vaccines; b) bacteriophage in measles and polio vaccines; c) reverse transcriptase in measles and mumps vaccines; and d) porcine circovirus and porcine circovirus DNA sequences in rotavirus vaccines. The lessons learned from each event are discussed. Based in part on those experiences, certain scientific principles have been identified by WHO that should be considered in regulatory risk evaluation if an adventitious agent is found in a marketed vaccine in the future. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Opinion dynamics of learning agents: does seeking consensus lead to disagreement?

International Nuclear Information System (INIS)

Vicente, Renato; Martins, André C R; Caticha, Nestor

2009-01-01

We study opinion dynamics in a population of interacting adaptive agents voting on a set of issues represented by vectors. We consider agents who can classify issues into one of two categories and can arrive at their opinions using an adaptive algorithm. Adaptation comes from learning and the information for the learning process comes from interacting with other neighboring agents and trying to change the internal state in order to concur with their opinions. The change in the internal state is driven by the information contained in the issue and in the opinion of the other agent. We present results in a simple yet rich context where each agent uses a Boolean perceptron to state their opinion. If the update occurs with information asynchronously exchanged among pairs of agents, then the typical case, if the number of issues is kept small, is the evolution into a society torn by the emergence of factions with extreme opposite beliefs. This occurs even when seeking consensus with agents with opposite opinions. If the number of issues is large, the dynamics becomes trapped, the society does not evolve into factions and a distribution of moderate opinions is observed. The synchronous case is technically simpler and is studied by formulating the problem in terms of differential equations that describe the evolution of order parameters that measure the consensus between pairs of agents. We show that for a large number of issues and unidirectional information flow, global consensus is a fixed point; however, the approach to this consensus is glassy for large societies

Opinion dynamics of learning agents: does seeking consensus lead to disagreement?

Science.gov (United States)

Vicente, Renato; Martins, André C. R.; Caticha, Nestor

2009-03-01

We study opinion dynamics in a population of interacting adaptive agents voting on a set of issues represented by vectors. We consider agents who can classify issues into one of two categories and can arrive at their opinions using an adaptive algorithm. Adaptation comes from learning and the information for the learning process comes from interacting with other neighboring agents and trying to change the internal state in order to concur with their opinions. The change in the internal state is driven by the information contained in the issue and in the opinion of the other agent. We present results in a simple yet rich context where each agent uses a Boolean perceptron to state their opinion. If the update occurs with information asynchronously exchanged among pairs of agents, then the typical case, if the number of issues is kept small, is the evolution into a society torn by the emergence of factions with extreme opposite beliefs. This occurs even when seeking consensus with agents with opposite opinions. If the number of issues is large, the dynamics becomes trapped, the society does not evolve into factions and a distribution of moderate opinions is observed. The synchronous case is technically simpler and is studied by formulating the problem in terms of differential equations that describe the evolution of order parameters that measure the consensus between pairs of agents. We show that for a large number of issues and unidirectional information flow, global consensus is a fixed point; however, the approach to this consensus is glassy for large societies.

Designing Multimedia Learning Environments Using Animated Pedagogical Agents: Factors and Issues

Science.gov (United States)

Woo, H. L.

2009-01-01

Animated pedagogical agents (APAs) are known to possess great potential in supporting learning because of their ability to simulate a real classroom learning environment. But research in this area has produced mixed results. The reason for this remains puzzling. This paper is written with two purposes: (1) to examine some recent research and…

Linguistic Models at the Crossroads of Agents, Learning and Formal Languages

Directory of Open Access Journals (Sweden)

Leonor BECERRA-BONACHE

2014-12-01

Full Text Available This paper aims at reviewing the most relevant linguistic applications developed in the intersection between three different fields: machine learning, formal language theory and agent technologies. On the one hand, we present some of the main linguistic contributions of the intersection between machine learning and formal languages, which constitutes a well-established research area known as Grammatical Inference. On the other hand, we present an overview of the main linguistic applications of models developed in the intersection between agent technologies and formal languages, such as colonies, grammar systems and eco-grammar systems. Our goal is to show how interdisciplinary research between these three fields can contribute to better understand how natural language is acquired and processed.

Mirror Neurons, Embodied Cognitive Agents and Imitation Learning

Czech Academy of Sciences Publication Activity Database

Wiedermann, Jiří

2003-01-01

Roč. 22, č. 6 (2003), s. 545-559 ISSN 1335-9150 R&D Projects: GA ČR GA201/02/1456 Institutional research plan: CEZ:AV0Z1030915 Keywords : complete agents * mirror neurons * embodied cognition * imitation learning * sensorimotor control Subject RIV: BA - General Mathematics Impact factor: 0.254, year: 2003 http://www.cai.sk/ojs/index.php/cai/article/view/468

N-grams Based Supervised Machine Learning Model for Mobile Agent Platform Protection against Unknown Malicious Mobile Agents

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Pallavi Bagga

2017-12-01

Full Text Available From many past years, the detection of unknown malicious mobile agents before they invade the Mobile Agent Platform has been the subject of much challenging activity. The ever-growing threat of malicious agents calls for techniques for automated malicious agent detection. In this context, the machine learning (ML methods are acknowledged more effective than the Signature-based and Behavior-based detection methods. Therefore, in this paper, the prime contribution has been made to detect the unknown malicious mobile agents based on n-gram features and supervised ML approach, which has not been done so far in the sphere of the Mobile Agents System (MAS security. To carry out the study, the n-grams ranging from 3 to 9 are extracted from a dataset containing 40 malicious and 40 non-malicious mobile agents. Subsequently, the classification is performed using different classifiers. A nested 5-fold cross validation scheme is employed in order to avoid the biasing in the selection of optimal parameters of classifier. The observations of extensive experiments demonstrate that the work done in this paper is suitable for the task of unknown malicious mobile agent detection in a Mobile Agent Environment, and also adds the ML in the interest list of researchers dealing with MAS security.

The chemotherapeutic agent paclitaxel selectively impairs reversal learning while sparing prior learning, new learning and episodic memory.

Science.gov (United States)

Panoz-Brown, Danielle; Carey, Lawrence M; Smith, Alexandra E; Gentry, Meredith; Sluka, Christina M; Corbin, Hannah E; Wu, Jie-En; Hohmann, Andrea G; Crystal, Jonathon D

2017-10-01

Chemotherapy is widely used to treat patients with systemic cancer. The efficacy of cancer therapies is frequently undermined by adverse side effects that have a negative impact on the quality of life of cancer survivors. Cancer patients who receive chemotherapy often experience chemotherapy-induced cognitive impairment across a variety of domains including memory, learning, and attention. In the current study, the impact of paclitaxel, a taxane derived chemotherapeutic agent, on episodic memory, prior learning, new learning, and reversal learning were evaluated in rats. Neurogenesis was quantified post-treatment in the dentate gyrus of the same rats using immunostaining for 5-Bromo-2'-deoxyuridine (BrdU) and Ki67. Paclitaxel treatment selectively impaired reversal learning while sparing episodic memory, prior learning, and new learning. Furthermore, paclitaxel-treated rats showed decreases in markers of hippocampal cell proliferation, as measured by markers of cell proliferation assessed using immunostaining for Ki67 and BrdU. This work highlights the importance of using multiple measures of learning and memory to identify the pattern of impaired and spared aspects of chemotherapy-induced cognitive impairment. Copyright © 2017 Elsevier Inc. All rights reserved.

A learning-based agent for home neurorehabilitation.

Science.gov (United States)

Lydakis, Andreas; Meng, Yuanliang; Munroe, Christopher; Wu, Yi-Ning; Begum, Momotaz

2017-07-01

This paper presents the iterative development of an artificially intelligent system to promote home-based neurorehabilitation. Although proper, structured practice of rehabilitation exercises at home is the key to successful recovery of motor functions, there is no home-program out there which can monitor a patient's exercise-related activities and provide corrective feedback in real time. To this end, we designed a Learning from Demonstration (LfD) based home-rehabilitation framework that combines advanced robot learning algorithms with commercially available wearable technologies. The proposed system uses exercise-related motion information and electromyography signals (EMG) of a patient to train a Markov Decision Process (MDP). The trained MDP model can enable an agent to serve as a coach for a patient. On a system level, this is the first initiative, to the best of our knowledge, to employ LfD in an health-care application to enable lay users to program an intelligent system. From a rehabilitation research perspective, this is a completely novel initiative to employ machine learning to provide interactive corrective feedback to a patient in home settings.

Agent-oriented Modeling for Collaborative Learning Environments: A Peer-to-Peer Helpdesk Case Study

NARCIS (Netherlands)

Guizzardi-Silva Souza, R.; Wagner, G.; Aroyo, L.M.

2002-01-01

In this paper, we present the analysis and modelling of Help&Learn, an agent-based peer-to-peer helpdesk system to support extra-class interactions among students and teachers. Help&Learn expands the student’s possibility of solving problems, getting involved in a cooperative learning experience

A Distributed Multi-Agent System for Collaborative Information Management and Learning

Science.gov (United States)

Chen, James R.; Wolfe, Shawn R.; Wragg, Stephen D.; Koga, Dennis (Technical Monitor)

2000-01-01

In this paper, we present DIAMS, a system of distributed, collaborative agents to help users access, manage, share and exchange information. A DIAMS personal agent helps its owner find information most relevant to current needs. It provides tools and utilities for users to manage their information repositories with dynamic organization and virtual views. Flexible hierarchical display is integrated with indexed query search-to support effective information access. Automatic indexing methods are employed to support user queries and communication between agents. Contents of a repository are kept in object-oriented storage to facilitate information sharing. Collaboration between users is aided by easy sharing utilities as well as automated information exchange. Matchmaker agents are designed to establish connections between users with similar interests and expertise. DIAMS agents provide needed services for users to share and learn information from one another on the World Wide Web.

Learning Agent for a Heat-Pump Thermostat with a Set-Back Strategy Using Model-Free Reinforcement Learning

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Frederik Ruelens

2015-08-01

Full Text Available The conventional control paradigm for a heat pump with a less efficient auxiliary heating element is to keep its temperature set point constant during the day. This constant temperature set point ensures that the heat pump operates in its more efficient heat-pump mode and minimizes the risk of activating the less efficient auxiliary heating element. As an alternative to a constant set-point strategy, this paper proposes a learning agent for a thermostat with a set-back strategy. This set-back strategy relaxes the set-point temperature during convenient moments, e.g., when the occupants are not at home. Finding an optimal set-back strategy requires solving a sequential decision-making process under uncertainty, which presents two challenges. The first challenge is that for most residential buildings, a description of the thermal characteristics of the building is unavailable and challenging to obtain. The second challenge is that the relevant information on the state, i.e., the building envelope, cannot be measured by the learning agent. In order to overcome these two challenges, our paper proposes an auto-encoder coupled with a batch reinforcement learning technique. The proposed approach is validated for two building types with different thermal characteristics for heating in the winter and cooling in the summer. The simulation results indicate that the proposed learning agent can reduce the energy consumption by 4%–9% during 100 winter days and by 9%–11% during 80 summer days compared to the conventional constant set-point strategy.

An Autonomous Mobile Agent-Based Distributed Learning Architecture-A Proposal and Analytical Analysis

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I. Ahmed M. J. SADIIG

2005-10-01

Full Text Available An Autonomous Mobile Agent-Based Distributed Learning Architecture-A Proposal and Analytical Analysis Dr. I. Ahmed M. J. SADIIG Department of Electrical & Computer EngineeringInternational Islamic University GombakKuala Lumpur-MALAYSIA ABSTRACT The traditional learning paradigm invoving face-to-face interaction with students is shifting to highly data-intensive electronic learning with the advances in Information and Communication Technology. An important component of the e-learning process is the delivery of the learning contents to their intended audience over a network. A distributed learning system is dependent on the network for the efficient delivery of its contents to the user. However, as the demand of information provision and utilization increases on the Internet, the current information service provision and utilization methods are becoming increasingly inefficient. Although new technologies have been employed for efficient learning methodologies within the context of an e-learning environment, the overall efficiency of the learning system is dependent on the mode of distribution and utilization of its learning contents. It is therefore imperative to employ new techniques to meet the service demands of current and future e-learning systems. In this paper, an architecture based on autonomous mobile agents creating a Faded Information Field is proposed. Unlike the centralized information distribution in a conventional e-learning system, the information is decentralized in the proposed architecture resulting in increased efficiency of the overall system for distribution and utilization of system learning contents efficiently and fairly. This architecture holds the potential to address the heterogeneous user requirements as well as the changing conditions of the underlying network.

Adaptive Load Balancing of Parallel Applications with Multi-Agent Reinforcement Learning on Heterogeneous Systems

Directory of Open Access Journals (Sweden)

Johan Parent

2004-01-01

Full Text Available We report on the improvements that can be achieved by applying machine learning techniques, in particular reinforcement learning, for the dynamic load balancing of parallel applications. The applications being considered in this paper are coarse grain data intensive applications. Such applications put high pressure on the interconnect of the hardware. Synchronization and load balancing in complex, heterogeneous networks need fast, flexible, adaptive load balancing algorithms. Viewing a parallel application as a one-state coordination game in the framework of multi-agent reinforcement learning, and by using a recently introduced multi-agent exploration technique, we are able to improve upon the classic job farming approach. The improvements are achieved with limited computation and communication overhead.

An agent architecture with on-line learning of both procedural and declarative knowledge

Energy Technology Data Exchange (ETDEWEB)

Sun, R.; Peterson, T.; Merrill, E. [Univ. of Alabama, Tuscaloosa, AL (United States)

1996-12-31

In order to develop versatile cognitive agents that learn in situated contexts and generalize resulting knowledge to different environments, we explore the possibility of learning both declarative and procedural knowledge in a hybrid connectionist architecture. The architecture is based on the two-level idea proposed earlier by the author. Declarative knowledge is represented symbolically, while procedural knowledge is represented subsymbolically. The architecture integrates reactive procedures, rules, learning, and decision-making in a unified framework, and structures different learning components (including Q-learning and rule induction) in a synergistic way to perform on-line and integrated learning.

Fostering Multimedia Learning of Science: Exploring the Role of an Animated Agent's Image

Science.gov (United States)

Dunsworth, Qi; Atkinson, Robert K.

2007-01-01

Research suggests that students learn better when studying a picture coupled with narration rather than on-screen text in a computer-based multimedia learning environment. Moreover, combining narration with the visual presence of an animated pedagogical agent may also encourage students to process information deeper than narration or on-screen…

Mirror Neurons, Embodied Cognitive Agents and Imitation Learning

OpenAIRE

Wiedermann, Jiří

2003-01-01

Mirror neurons are a relatively recent discovery; it has been conjectured that these neurons play an important role in imitation learning and other cognitive phenomena. We will study a possible place and role of mirror neurons in the neural architecture of embodied cognitive agents. We will formulate and investigate the hypothesis that mirror neurons serve as a mechanism which coordinates the multimodal (i.e., motor, perceptional and proprioceptive) information and completes it so that the ag...

The efficiency of the RULES-4 classification learning algorithm in predicting the density of agents

Directory of Open Access Journals (Sweden)

Ziad Salem

2014-12-01

Full Text Available Learning is the act of obtaining new or modifying existing knowledge, behaviours, skills or preferences. The ability to learn is found in humans, other organisms and some machines. Learning is always based on some sort of observations or data such as examples, direct experience or instruction. This paper presents a classification algorithm to learn the density of agents in an arena based on the measurements of six proximity sensors of a combined actuator sensor units (CASUs. Rules are presented that were induced by the learning algorithm that was trained with data-sets based on the CASU’s sensor data streams collected during a number of experiments with “Bristlebots (agents in the arena (environment”. It was found that a set of rules generated by the learning algorithm is able to predict the number of bristlebots in the arena based on the CASU’s sensor readings with satisfying accuracy.

Determinants of E-learning Acceptance among Agricultural Extension Agents in Malaysia: A Conceptual Framework

OpenAIRE

Mangir, Safaie; Othman, Zakirah; Udin, Zulkifli Mohamed

2016-01-01

The objective of this paper is to develop a framework on e-learning acceptance among agricultural extension agents in Malaysian agricultural sector. E-learning is viewed as a solution in response to the increasing need for learning and training. This paper will review past literatures for the relevant factors that influence behavioral intention for e-learning acceptance as well as the relevant behavioral theories that provide the foundation for developing research framework to illustrate the ...

Animated pedagogical agents effects on enhancing student motivation and learning in a science inquiry learning environment

NARCIS (Netherlands)

van der Meij, Hans; van der Meij, Jan; Harmsen, Ruth

This study focuses on the design and testing of a motivational animated pedagogical agent (APA) in an inquiry learning environment on kinematics. The aim of including the APA was to enhance students’ perceptions of task relevance and selfefficacy. Given the under-representation of girls in science

Animated pedagogical agents effects on enhancing student motivation and learning in a science inquiry learning environment

NARCIS (Netherlands)

van der Meij, Hans; van der Meij, Jan; Harmsen, Ruth

2015-01-01

This study focuses on the design and testing of a motivational animated pedagogical agent (APA) in an inquiry learning environment on kinematics. The aim of including the APA was to enhance students’ perceptions of task relevance and self-efficacy. Given the under-representation of girls in science

The Cost of Performance? Students' Learning about Acting as Change Agents in Their Schools

Science.gov (United States)

Kehoe, Ian

2015-01-01

This paper explores how performance culture could affect students' learning about, and disposition towards, acting as organisational change agents in schools. This is based on findings from an initiative aimed to enable students to experience acting as change agents on an aspect of the school's culture that concerned them. The initiative was…

Contrast agents provide a faster learning curve in dipyridamole stress echocardiography.

Science.gov (United States)

Zamorano, Jose; Sánchez, Violeta; Moreno, Raúl; Almería, Carlos; Rodrigo, Jose; Serra, Viviana; Azcona, Luis; Aubele, Adalia; Mataix, Luis; Sánchez-Harguindey, Luis

2002-12-01

Interobserver variability is an important limitation of the stress echocardiography and depends on the echocardiographer training. Our aim was to evaluate if the use of contrast agents during dipyridamole stress echocardiography would improve the agreement between an experienced and a non-experienced observer in stress echo and therefore if contrast would affect the learning period of dypyridamole stress echo. Two independent observers without knowledge of any patient data interpreted all stress studies. One observer was an experienced one and the other had experience in echocardiography but not in stress echo. Two observers analysed 87 non-selected and consecutive studies. Out of the 87 studies, 46 were performed without contrast administration, whereas i.v. contrast (2.5 g Levovist by two bolus at rest and at peak stress) was administered in 41. In all cases, second harmonic imaging and stress digitalisation pack was used. The agreement between observers showed a kappa index of 0.58 and 0.83 without and with contrast administration, respectively. The use of contrast agents provides a better agreement in the evaluation of stress echo between an experienced and a non-experienced observer in stress echo. Adding routinely contrast agents could probably reduce the number of exams required for the necessary learning curve in stress echocardiography.

Cooperative learning neural network output feedback control of uncertain nonlinear multi-agent systems under directed topologies

Science.gov (United States)

Wang, W.; Wang, D.; Peng, Z. H.

2017-09-01

Without assuming that the communication topologies among the neural network (NN) weights are to be undirected and the states of each agent are measurable, the cooperative learning NN output feedback control is addressed for uncertain nonlinear multi-agent systems with identical structures in strict-feedback form. By establishing directed communication topologies among NN weights to share their learned knowledge, NNs with cooperative learning laws are employed to identify the uncertainties. By designing NN-based κ-filter observers to estimate the unmeasurable states, a new cooperative learning output feedback control scheme is proposed to guarantee that the system outputs can track nonidentical reference signals with bounded tracking errors. A simulation example is given to demonstrate the effectiveness of the theoretical results.

Cloud Computing and Multi Agent System to improve Learning Object Paradigm

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Ana B. Gil

2015-05-01

Full Text Available The paradigm of Learning Object provides Educators and Learners with the ability to access an extensive number of learning resources. To do so, this paradigm provides different technologies and tools, such as federated search platforms and storage repositories, in order to obtain information ubiquitously and on demand. However, the vast amount and variety of educational content, which is distributed among several repositories, and the existence of various and incompatible standards, technologies and interoperability layers among repositories, constitutes a real problem for the expansion of this paradigm. This study presents an agent-based architecture that uses the advantages provided by Cloud Computing platforms to deal with the open issues on the Learning Object paradigm.

Animated Pedagogical Agents Effects on Enhancing Student Motivation and Learning in a Science Inquiry Learning Environment

Science.gov (United States)

van der Meij, Hans; van der Meij, Jan; Harmsen, Ruth

2015-01-01

This study focuses on the design and testing of a motivational animated pedagogical agent (APA) in an inquiry learning environment on kinematics. The aim of including the APA was to enhance students' perceptions of task relevance and self-efficacy. Given the under-representation of girls in science classrooms, special attention was given to…

The Effects of a Pedagogical Agent's Smiling Expression on the Learner's Emotions and Motivation in a Virtual Learning Environment

Science.gov (United States)

Liew, Tze Wei; Zin, Nor Azan Mat; Sahari, Noraidah; Tan, Su-Mae

2016-01-01

The present study aimed to test the hypothesis that a smiling expression on the face of a talking pedagogical agent could positively affect a learner's emotions, motivation, and learning outcomes in a virtual learning environment. Contrary to the hypothesis, results from Experiment 1 demonstrated that the pedagogical agent's smile induced negative…

An Agent-based Approach for Ideational Support in Learning - Integration and Impact.

NARCIS (Netherlands)

Aroyo, L.M.; Stoyanov, S.; Kommers, Petrus A.M.

1999-01-01

This paper provides results from research work done in respect to the application of agent technology within educational settings. It focuses on problem solving, information handling issues and idea generation. It is based on two research system examples: Solution, Mapping, Intelligent, Learning,

Supporting Multimedia Learning with Visual Signalling and Animated Pedagogical Agent: Moderating Effects of Prior Knowledge

Science.gov (United States)

Johnson, A. M.; Ozogul, G.; Reisslein, M.

2015-01-01

An experiment examined the effects of visual signalling to relevant information in multiple external representations and the visual presence of an animated pedagogical agent (APA). Students learned electric circuit analysis using a computer-based learning environment that included Cartesian graphs, equations and electric circuit diagrams. The…

Adaptive learning in agents behaviour: A framework for electricity markets simulation

DEFF Research Database (Denmark)

Pinto, Tiago; Vale, Zita; Sousa, Tiago M.

2014-01-01

decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology...... that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management...... allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides...

A Two-Stage Multi-Agent Based Assessment Approach to Enhance Students' Learning Motivation through Negotiated Skills Assessment

Science.gov (United States)

Chadli, Abdelhafid; Bendella, Fatima; Tranvouez, Erwan

2015-01-01

In this paper we present an Agent-based evaluation approach in a context of Multi-agent simulation learning systems. Our evaluation model is based on a two stage assessment approach: (1) a Distributed skill evaluation combining agents and fuzzy sets theory; and (2) a Negotiation based evaluation of students' performance during a training…

TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow

OpenAIRE

Hafner, Danijar; Davidson, James; Vanhoucke, Vincent

2017-01-01

We introduce TensorFlow Agents, an efficient infrastructure paradigm for building parallel reinforcement learning algorithms in TensorFlow. We simulate multiple environments in parallel, and group them to perform the neural network computation on a batch rather than individual observations. This allows the TensorFlow execution engine to parallelize computation, without the need for manual synchronization. Environments are stepped in separate Python processes to progress them in parallel witho...

A Q-learning agent-based model for the analysis of the power market dynamics

International Nuclear Information System (INIS)

Tellidou, A.; Bakirtzis, A.

2006-01-01

The introduction of deregulation in the electricity sector resulted in a different way of thinking and acting on the part of producers. Power suppliers strive to maximize their profit and their utilization rate through a bidding process. According to the pricing system, the competition conditions, the demand side bidding, and the available information, they develop different bidding strategies in order to exploit every possible advantage. This paper presents the Q-Learning algorithm in order to model the bidding strategy of suppliers in electricity auctions. The study examined players' behaviour in the spot market and the change in their policy under different conditions of demand. The Q-learning algorithm considers a novel approach to the definition of states and actions. States are not defined exclusively, as states of the environment, but rather, are different for each agent and relative to the impact the environment has on the agent. Actions are not represented by the price the agent bids, but by the variation between the previous and the new bid price. Market structure was described in this paper and the supplier's bidding problem was formulated in terms of Q-learning. A description of the test system was presented and the parameter selection of the algorithm, as well as the presentation and the results of four case study simulations were discussed. The Q-learning algorithm in supplier bidding strategy showed very promising results. it was suggested that the research should be expanded to include more producers or tests of transmission systems. 9 refs., 2 tabs., 6 figs

“Toward socially responsible agents: integrating attachment and learning in emotional decision-making,”

OpenAIRE

M. Ben Moussa and N. Magnenat-Thalmann

2013-01-01

Our goal is to create socially responsible agents either robots or virtual humans. In this paper we present an integration of emotions attachment and learning in emotional decision making to achieve this goal. Based on emerging psychological theories we aim at building human like emotional decision making where emotions play a central role in selecting the next action to be performed by the agent. Here we present our own approach for emotion appraisal where we use emotional attachment as an i...

Persuasive Conversational Agent with Persuasion Tactics

Science.gov (United States)

Narita, Tatsuya; Kitamura, Yasuhiko

Persuasive conversational agents persuade people to change their attitudes or behaviors through conversation, and are expected to be applied as virtual sales clerks in e-shopping sites. As an approach to create such an agent, we have developed a learning agent with the Wizard of Oz method in which a person called Wizard talks to the user pretending to be the agent. The agent observes the conversations between the Wizard and the user, and learns how to persuade people. In this method, the Wizard has to reply to most of the user's inputs at the beginning, but the burden gradually falls because the agent learns how to reply as the conversation model grows.

Homeostatic Agent for General Environment

Science.gov (United States)

Yoshida, Naoto

2018-03-01

One of the essential aspect in biological agents is dynamic stability. This aspect, called homeostasis, is widely discussed in ethology, neuroscience and during the early stages of artificial intelligence. Ashby's homeostats are general-purpose learning machines for stabilizing essential variables of the agent in the face of general environments. However, despite their generality, the original homeostats couldn't be scaled because they searched their parameters randomly. In this paper, first we re-define the objective of homeostats as the maximization of a multi-step survival probability from the view point of sequential decision theory and probabilistic theory. Then we show that this optimization problem can be treated by using reinforcement learning algorithms with special agent architectures and theoretically-derived intrinsic reward functions. Finally we empirically demonstrate that agents with our architecture automatically learn to survive in a given environment, including environments with visual stimuli. Our survival agents can learn to eat food, avoid poison and stabilize essential variables through theoretically-derived single intrinsic reward formulations.

Safiye Erol’un Dineyri Papazı Romanında Bireyleşim/Kemalat Yolculuğu The Process of Individuation in Dineyri Papazı by Safiye Erol

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Gülsemin HAZER

2012-09-01

Full Text Available In Dineyri Papazı of Safiye Erol is told a young girl's process of individuation. In analytical psychology the journey of individuation is expressed in to recognize as with all aspects of the hero himself. In this novel, this situation is illustrated with sufism process is passed to the universal human way of being. In both cases, the hero must live a journey full of exams and reeling. At last, the individual who reach spiritual integrity will converge to the value of self and be happy. Tthis journey which occur on a circular line can be read and resolution with both according to the “seyr u sülûk” in sufism and the help of the stages seperation- initiation- return which Joseph Campbell put in the Endless Journey of the Hero. This novel moves the follows from Safiye Erol's private life. In this regard, supplies an autobiographical feature. It is the most important work for her. Like in her other novels, the writer gets the love to the center in her this last novel, too. In the plane of the narrative content, the writer tells a young girl's spirit pains who believes that love has found. Protagonist lives to love in it's purest form, her inner world reveal with deep psychological an alyzes and all the nudity. The places which the hero make journey of initiation in there, the people who are in relationship with her and some concepts/icons serve to formation of this journey. With this journey, the hero of the novel realize his awareness and move to another dimension to this love. Also, this journey is mean increased reaching survivability. The individual recognizes to latent powers within himself and embraces to infinite happiness. Safiye Erol’un, Dineyri Papazı adlı romanında bir genç kızın bireyleşim/kemalat süreci anlatılmaktadır. Analitik psikolojide kahramanın kendisini bütün yönleriyle tanıması olarak ifade edilen bireyleşim yolculuğunu, tasavvuf kâmil insan olma yolunda geçirilen süreçle izah eder. Her iki halde

Reinforcement Learning Multi-Agent Modeling of Decision-Making Agents for the Study of Transboundary Surface Water Conflicts with Application to the Syr Darya River Basin

Science.gov (United States)

Riegels, N.; Siegfried, T.; Pereira Cardenal, S. J.; Jensen, R. A.; Bauer-Gottwein, P.

2008-12-01

In most economics--driven approaches to optimizing water use at the river basin scale, the system is modelled deterministically with the goal of maximizing overall benefits. However, actual operation and allocation decisions must be made under hydrologic and economic uncertainty. In addition, river basins often cross political boundaries, and different states may not be motivated to cooperate so as to maximize basin- scale benefits. Even within states, competing agents such as irrigation districts, municipal water agencies, and large industrial users may not have incentives to cooperate to realize efficiency gains identified in basin- level studies. More traditional simulation--optimization approaches assume pre-commitment by individual agents and stakeholders and unconditional compliance on each side. While this can help determine attainable gains and tradeoffs from efficient management, such hardwired policies do not account for dynamic feedback between agents themselves or between agents and their environments (e.g. due to climate change etc.). In reality however, we are dealing with an out-of-equilibrium multi-agent system, where there is neither global knowledge nor global control, but rather continuous strategic interaction between decision making agents. Based on the theory of stochastic games, we present a computational framework that allows for studying the dynamic feedback between decision--making agents themselves and an inherently uncertain environment in a spatially and temporally distributed manner. Agents with decision-making control over water allocation such as countries, irrigation districts, and municipalities are represented by reinforcement learning agents and coupled to a detailed hydrologic--economic model. This approach emphasizes learning by agents from their continuous interaction with other agents and the environment. It provides a convenient framework for the solution of the problem of dynamic decision-making in a mixed cooperative / non

The chemotherapeutic agent paclitaxel selectively impairs learning while sparing source memory and spatial memory.

Science.gov (United States)

Smith, Alexandra E; Slivicki, Richard A; Hohmann, Andrea G; Crystal, Jonathon D

2017-03-01

Chemotherapeutic agents are widely used to treat patients with systemic cancer. The efficacy of these therapies is undermined by their adverse side-effect profiles such as cognitive deficits that have a negative impact on the quality of life of cancer survivors. Cognitive side effects occur across a variety of domains, including memory, executive function, and processing speed. Such impairments are exacerbated under cognitive challenges and a subgroup of patients experience long-term impairments. Episodic memory in rats can be examined using a source memory task. In the current study, rats received paclitaxel, a taxane-derived chemotherapeutic agent, and learning and memory functioning was examined using the source memory task. Treatment with paclitaxel did not impair spatial and episodic memory, and paclitaxel treated rats were not more susceptible to cognitive challenges. Under conditions in which memory was not impaired, paclitaxel treatment impaired learning of new rules, documenting a decreased sensitivity to changes in experimental contingencies. These findings provide new information on the nature of cancer chemotherapy-induced cognitive impairments, particularly regarding the incongruent vulnerability of episodic memory and new learning following treatment with paclitaxel. Copyright © 2016 Elsevier B.V. All rights reserved.

Iterative learning control for multi-agent systems coordination

CERN Document Server

Yang, Shiping; Li, Xuefang; Shen, Dong

2016-01-01

A timely guide using iterative learning control (ILC) as a solution for multi-agent systems (MAS) challenges, this book showcases recent advances and industrially relevant applications. Readers are first given a comprehensive overview of the intersection between ILC and MAS, then introduced to a range of topics that include both basic and advanced theoretical discussions, rigorous mathematics, engineering practice, and both linear and nonlinear systems. Through systematic discussion of network theory and intelligent control, the authors explore future research possibilities, develop new tools, and provide numerous applications such as power grids, communication and sensor networks, intelligent transportation systems, and formation control. Readers will gain a roadmap of the latest advances in the fields and can use their newfound knowledge to design their own algorithms.

Asymptotically Optimal Agents

OpenAIRE

Lattimore, Tor; Hutter, Marcus

2011-01-01

Artificial general intelligence aims to create agents capable of learning to solve arbitrary interesting problems. We define two versions of asymptotic optimality and prove that no agent can satisfy the strong version while in some cases, depending on discounting, there does exist a non-computable weak asymptotically optimal agent.

Instructable autonomous agents. Ph.D. Thesis

Science.gov (United States)

Huffman, Scott Bradley

1994-01-01

In contrast to current intelligent systems, which must be laboriously programmed for each task they are meant to perform, instructable agents can be taught new tasks and associated knowledge. This thesis presents a general theory of learning from tutorial instruction and its use to produce an instructable agent. Tutorial instruction is a particularly powerful form of instruction, because it allows the instructor to communicate whatever kind of knowledge a student needs at whatever point it is needed. To exploit this broad flexibility, however, a tutorable agent must support a full range of interaction with its instructor to learn a full range of knowledge. Thus, unlike most machine learning tasks, which target deep learning of a single kind of knowledge from a single kind of input, tutorability requires a breadth of learning from a broad range of instructional interactions. The theory of learning from tutorial instruction presented here has two parts. First, a computational model of an intelligent agent, the problem space computational model, indicates the types of knowledge that determine an agent's performance, and thus, that should be acquirable via instruction. Second, a learning technique, called situated explanation specifies how the agent learns general knowledge from instruction. The theory is embodied by an implemented agent, Instructo-Soar, built within the Soar architecture. Instructo-Soar is able to learn hierarchies of completely new tasks, to extend task knowledge to apply in new situations, and in fact to acquire every type of knowledge it uses during task performance - control knowledge, knowledge of operators' effects, state inferences, etc. - from interactive natural language instructions. This variety of learning occurs by applying the situated explanation technique to a variety of instructional interactions involving a variety of types of instructions (commands, statements, conditionals, etc.). By taking seriously the requirements of flexible

Artificial agents learning human fairness

NARCIS (Netherlands)

Jong, de S.; Tuyls, K.P.; Verbeeck, K.; Padgham, xx; Parkes, xx

2008-01-01

Recent advances in technology allow multi-agent systems to be deployed in cooperation with or as a service for humans. Typically, those systems are designed assuming individually rational agents, according to the principles of classical game theory. However, research in the field of behavioral

Learning Agents for Autonomous Space Asset Management (LAASAM)

Science.gov (United States)

Scally, L.; Bonato, M.; Crowder, J.

2011-09-01

Current and future space systems will continue to grow in complexity and capabilities, creating a formidable challenge to monitor, maintain, and utilize these systems and manage their growing network of space and related ground-based assets. Integrated System Health Management (ISHM), and in particular, Condition-Based System Health Management (CBHM), is the ability to manage and maintain a system using dynamic real-time data to prioritize, optimize, maintain, and allocate resources. CBHM entails the maintenance of systems and equipment based on an assessment of current and projected conditions (situational and health related conditions). A complete, modern CBHM system comprises a number of functional capabilities: sensing and data acquisition; signal processing; conditioning and health assessment; diagnostics and prognostics; and decision reasoning. In addition, an intelligent Human System Interface (HSI) is required to provide the user/analyst with relevant context-sensitive information, the system condition, and its effect on overall situational awareness of space (and related) assets. Colorado Engineering, Inc. (CEI) and Raytheon are investigating and designing an Intelligent Information Agent Architecture that will provide a complete range of CBHM and HSI functionality from data collection through recommendations for specific actions. The research leverages CEI’s expertise with provisioning management network architectures and Raytheon’s extensive experience with learning agents to define a system to autonomously manage a complex network of current and future space-based assets to optimize their utilization.

Towards Culturally-Aware Virtual Agent Systems

DEFF Research Database (Denmark)

Endrass, Birgit; André, Elisabeth; Rehm, Matthias

2010-01-01

Globalization leads to an increase in intercultural encounters with a risk of misunderstandings due to different patterns of behavior and understanding. Learning applications have been proposed that employ virtual agents as their primary tool. Through their embodiment, learning can be done...... in a game-like environment in a more interesting way than for example learning with a textbook. The authors support the idea that virtual agents are a great opportunity for teaching cultural awareness. Realizing this, the concept of culture needs to be translated into computational models and the advantages...... of different systems using virtual agents need to be considered. Therefore, the authors reflect in this chapter on how virtual agents can help to learn about culture, scan definitions of culture from the social sciences, give an overview on how multiagent systems developed over time and classify the state...

An intelligent agent for optimal river-reservoir system management

Science.gov (United States)

Rieker, Jeffrey D.; Labadie, John W.

2012-09-01

A generalized software package is presented for developing an intelligent agent for stochastic optimization of complex river-reservoir system management and operations. Reinforcement learning is an approach to artificial intelligence for developing a decision-making agent that learns the best operational policies without the need for explicit probabilistic models of hydrologic system behavior. The agent learns these strategies experientially in a Markov decision process through observational interaction with the environment and simulation of the river-reservoir system using well-calibrated models. The graphical user interface for the reinforcement learning process controller includes numerous learning method options and dynamic displays for visualizing the adaptive behavior of the agent. As a case study, the generalized reinforcement learning software is applied to developing an intelligent agent for optimal management of water stored in the Truckee river-reservoir system of California and Nevada for the purpose of streamflow augmentation for water quality enhancement. The intelligent agent successfully learns long-term reservoir operational policies that specifically focus on mitigating water temperature extremes during persistent drought periods that jeopardize the survival of threatened and endangered fish species.

Mining Temporal Patterns to Improve Agents Behavior: Two Case Studies

Science.gov (United States)

Fournier-Viger, Philippe; Nkambou, Roger; Faghihi, Usef; Nguifo, Engelbert Mephu

We propose two mechanisms for agent learning based on the idea of mining temporal patterns from agent behavior. The first one consists of extracting temporal patterns from the perceived behavior of other agents accomplishing a task, to learn the task. The second learning mechanism consists in extracting temporal patterns from an agent's own behavior. In this case, the agent then reuses patterns that brought self-satisfaction. In both cases, no assumption is made on how the observed agents' behavior is internally generated. A case study with a real application is presented to illustrate each learning mechanism.

Teachable Agents and the Protégé Effect: Increasing the Effort Towards Learning

Science.gov (United States)

Chase, Catherine C.; Chin, Doris B.; Oppezzo, Marily A.; Schwartz, Daniel L.

2009-08-01

Betty's Brain is a computer-based learning environment that capitalizes on the social aspects of learning. In Betty's Brain, students instruct a character called a Teachable Agent (TA) which can reason based on how it is taught. Two studies demonstrate the protégé effect: students make greater effort to learn for their TAs than they do for themselves. The first study involved 8th-grade students learning biology. Although all students worked with the same Betty's Brain software, students in the TA condition believed they were teaching their TAs, while in another condition, they believed they were learning for themselves. TA students spent more time on learning activities (e.g., reading) and also learned more. These beneficial effects were most pronounced for lower achieving children. The second study used a verbal protocol with 5th-grade students to determine the possible causes of the protégé effect. As before, students learned either for their TAs or for themselves. Like study 1, students in the TA condition spent more time on learning activities. These children treated their TAs socially by attributing mental states and responsibility to them. They were also more likely to acknowledge errors by displaying negative affect and making attributions for the causes of failures. Perhaps having a TA invokes a sense of responsibility that motivates learning, provides an environment in which knowledge can be improved through revision, and protects students' egos from the psychological ramifications of failure.

An Interactive Tool for Creating Multi-Agent Systems and Interactive Agent-based Games

DEFF Research Database (Denmark)

Lund, Henrik Hautop; Pagliarini, Luigi

2011-01-01

Utilizing principles from parallel and distributed processing combined with inspiration from modular robotics, we developed the modular interactive tiles. As an educational tool, the modular interactive tiles facilitate the learning of multi-agent systems and interactive agent-based games...

Who is more efficient: Teacher or pedagogical agents?

Science.gov (United States)

Lee, Tien Tien; Mustapha, Nur Hanani

2017-05-01

The purpose of the study is to investigate the impact of pedagogical agent's and teacher's role on students' understanding and motivation in the learning of Electrochemistry. Interactive Multimedia Module with Pedagogical Agents, EC Lab (IMMPA EC Lab) was used in this study. IMMPA EC Lab consists of five subunits in Electrochemistry topic. The research was a non-equivalent control group quasi experimental design involving two treatment groups and one control group. The first treatment group studied Electrochemistry with expert agent (Professor T) while the second treatment group studied Electrochemistry with learning companion agent (Lisa). On the other hand, the control group learned Electrochemistry with their Chemistry teacher using the material in the IMMPA EC Lab. The study was conducted at a secondary science school in the Pasir Puteh district involving 74 form four students. The instruments used in this research were the Electrochemistry achievement tests in the form of pre-test and post-test, IMMPA EC Lab and motivation questionnaire. ANCOVA results found that there was no significant difference among the three groups in post-test. On the other hand, One-way ANOVA test proved that there were significant differences for the post-motivation scores between the control group and the treatment groups. Post motivation mean scores for expert agent treatment group and learning companion treatment group surpassed the control group. The study focus on the impact of pedagogical agents with different roles on students' learning and motivation should be promoted. Various versions of pedagogical agents that fulfil the good characteristics should be designed to enhance students' learning and motivation.

Agendas for Multi-Agent Learning

National Research Council Canada - National Science Library

Gordon, Geoffrey J

2006-01-01

.... We then consider research goals for modelling, design, and learning, and identify the problem of finding learning algorithms that guarantee convergence to Pareto-dominant equilibria against a wide range of opponents...

Prototype of Emapps.com Environment as Agent for Building the Learning Communities

Directory of Open Access Journals (Sweden)

Vilma Butkute

2010-04-01

Full Text Available The Information Society and Education need to be combined in order to achieve successful active citizenship and economical development with a natural and mutual interdependency. Project eMapps.com game platform can be an example of cross- connected eLearning, mobile and life environment contribution to education. It can increase effectiveness of education both for educational needs in XXI Century and to create a basis for further research on ICT mediation in Information Society. The positive outcomes on learners motivation are explored by the scientific modelling of the future educational environment prototype as agent for building up the learning communities of common intelligence at internal, local and international level. The key finding of this paper is that an eMapps.com game platform prototype can be used to ensure that technology, pedagogy and social networking context are closely aligned in order to realise the educational stimulation in secondary education.

Reinforcement Learning for a New Piano Mover

Directory of Open Access Journals (Sweden)

Yuko Ishiwaka

2005-08-01

Full Text Available We attempt to achieve corporative behavior of autonomous decentralized agents constructed via Q-Learning, which is a type of reinforcement learning. As such, in the present paper, we examine the piano mover's problem. We propose a multi-agent architecture that has a training agent, learning agents and intermediate agent. Learning agents are heterogeneous and can communicate with each other. The movement of an object with three kinds of agent depends on the composition of the actions of the learning agents. By learning its own shape through the learning agents, avoidance of obstacles by the object is expected. We simulate the proposed method in a two-dimensional continuous world. Results obtained in the present investigation reveal the effectiveness of the proposed method.

Applying Maxi-adjustment to Adaptive Information Filtering Agents

OpenAIRE

Lau, Raymond; ter Hofstede, Arthur H. M.; Bruza, Peter D.

2000-01-01

Learning and adaptation is a fundamental property of intelligent agents. In the context of adaptive information filtering, a filtering agent's beliefs about a user's information needs have to be revised regularly with reference to the user's most current information preferences. This learning and adaptation process is essential for maintaining the agent's filtering performance. The AGM belief revision paradigm provides a rigorous foundation for modelling rational and minimal changes to an age...

9th KES Conference on Agent and Multi-Agent Systems : Technologies and Applications

CERN Document Server

Howlett, Robert; Jain, Lakhmi

2015-01-01

Agents and multi-agent systems are related to a modern software paradigm which has long been recognized as a promising technology for constructing autonomous, complex and intelligent systems. The topics covered in this volume include agent-oriented software engineering, agent co-operation, co-ordination, negotiation, organization and communication, distributed problem solving, specification of agent communication languages, agent privacy, safety and security, formalization of ontologies and conversational agents. The volume highlights new trends and challenges in agent and multi-agent research and includes 38 papers classified in the following specific topics: learning paradigms, agent-based modeling and simulation, business model innovation and disruptive technologies, anthropic-oriented computing, serious games and business intelligence, design and implementation of intelligent agents and multi-agent systems, digital economy, and advances in networked virtual enterprises. Published p...

A novel multi-agent decentralized win or learn fast policy hill-climbing with eligibility trace algorithm for smart generation control of interconnected complex power grids

International Nuclear Information System (INIS)

Xi, Lei; Yu, Tao; Yang, Bo; Zhang, Xiaoshun

2015-01-01

Highlights: • Proposing a decentralized smart generation control scheme for the automatic generation control coordination. • A novel multi-agent learning algorithm is developed to resolve stochastic control problems in power systems. • A variable learning rate are introduced base on the framework of stochastic games. • A simulation platform is developed to test the performance of different algorithms. - Abstract: This paper proposes a multi-agent smart generation control scheme for the automatic generation control coordination in interconnected complex power systems. A novel multi-agent decentralized win or learn fast policy hill-climbing with eligibility trace algorithm is developed, which can effectively identify the optimal average policies via a variable learning rate under various operation conditions. Based on control performance standards, the proposed approach is implemented in a flexible multi-agent stochastic dynamic game-based smart generation control simulation platform. Based on the mixed strategy and average policy, it is highly adaptive in stochastic non-Markov environments and large time-delay systems, which can fulfill automatic generation control coordination in interconnected complex power systems in the presence of increasing penetration of decentralized renewable energy. Two case studies on both a two-area load–frequency control power system and the China Southern Power Grid model have been done. Simulation results verify that multi-agent smart generation control scheme based on the proposed approach can obtain optimal average policies thus improve the closed-loop system performances, and can achieve a fast convergence rate with significant robustness compared with other methods

Unibot, a Universal Agent Architecture for Robots

Directory of Open Access Journals (Sweden)

Saša Mladenović

2017-01-01

Full Text Available Today there are numerous robots in different applications domains despite the fact that they still have limitations in perception, actuation and decision process. Consequently, robots usually have limited autonomy, they are domain specific or have difficulty to adapt on new environments. Learning is the property that makes an agent intelligent and the crucial property that a robot should have to proliferate into the human society. Embedding the learning ability into the robot may simplify the development of the robot control mechanism. The motivation for this research is to develop the agent architecture of the universal robot – Unibot. In our approach the agent is the robot i.e. Unibot that acts in the physical world and is capable of learning. The Unibot conducts several simultaneous simulations of a problem of interest like path-finding. The novelty in our approach is the Multi-Agent Decision Support System which is developed and integrated into the Unibot agent architecture in order to execute simultaneous simulations. Furthermore, the Unibot calculates and evaluates between multiple solutions, decides which action should be performed and performs the action. The prototype of the Unibot agent architecture is described and evaluated in the experiment supported by the Lego Mindstorms robot and the NetLogo.

Learning-based diagnosis and repair

NARCIS (Netherlands)

Roos, Nico

2017-01-01

This paper proposes a new form of diagnosis and repair based on reinforcement learning. Self-interested agents learn locally which agents may provide a low quality of service for a task. The correctness of learned assessments of other agents is proved under conditions on exploration versus

Multi-issue Agent Negotiation Based on Fairness

Science.gov (United States)

Zuo, Baohe; Zheng, Sue; Wu, Hong

Agent-based e-commerce service has become a hotspot now. How to make the agent negotiation process quickly and high-efficiently is the main research direction of this area. In the multi-issue model, MAUT(Multi-attribute Utility Theory) or its derived theory usually consider little about the fairness of both negotiators. This work presents a general model of agent negotiation which considered the satisfaction of both negotiators via autonomous learning. The model can evaluate offers from the opponent agent based on the satisfaction degree, learn online to get the opponent's knowledge from interactive instances of history and negotiation of this time, make concessions dynamically based on fair object. Through building the optimal negotiation model, the bilateral negotiation achieved a higher efficiency and fairer deal.

14th International Conference on Practical Applications of Agents and Multi-Agent Systems : Special Sessions

CERN Document Server

Escalona, María; Corchuelo, Rafael; Mathieu, Philippe; Vale, Zita; Campbell, Andrew; Rossi, Silvia; Adam, Emmanuel; Jiménez-López, María; Navarro, Elena; Moreno, María

2016-01-01

PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an evolution of the International Workshop on Practical Applications of Agents and Multi-Agent Systems. PAAMS is an international yearly tribune to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development of Agents and Multi-Agent Systems. This volume presents the papers that have been accepted for the 2016 in the special sessions: Agents Behaviours and Artificial Markets (ABAM); Advances on Demand Response and Renewable Energy Sources in Agent Based Smart Grids (ADRESS); Agents and Mobile Devices (AM); Agent Methodologies for Intelligent Robotics Applications (AMIRA); Learning, Agents and Formal Languages (LAFLang); Multi-Agent Systems and Ambient Intelligence (MASMAI); Web Mining and ...

Survey of agent for intelligent information retrieval; Chiteki kensaku no tame no agent no chosa

Energy Technology Data Exchange (ETDEWEB)

Yazawa, T [Central Research Institute of Electric Power Industry, Tokyo (Japan)

1996-09-01

Development of agent systems has been surveyed, to classify and arrange characteristic functions of the agents, and to grasp the realization situation of these agents in their development. In addition, prospective functions of information retrieval systems using the agents at maximum and functions to be developed among these in the future are clarified. The agents are characterized by the expression function, communication function, planning function, adaptive function, and learning function. The agents are desired to be classified into interface agents whose works are to respond to individual workers, coordinator agents which conduct works with high pervasion, such as assignment of works and their control, and task agents which conduct specialized works for individual examples. Thus, design and configuration of the agent system, and improvement and expansion of system functions can be effectively and easily conducted. 52 refs., 5 figs., 3 tabs.

Teaching tourism change agents

DEFF Research Database (Denmark)

Blichfeldt, Bodil Stilling; Kvistgaard, Hans-Peter; Hird, John

2017-01-01

This article discuss es know ledge, competencies and skills Master’s students should obtain during their academic studies and particularly, the differences between teaching about a topic and teaching to do. This is ex emplified by experiential learning theory and the case of a change management...... course that is part of a Tourism Master’s program, where a major challenge is not only to teach students about change and change agents, but to teach them how change feels and ho w to become change agents. The c hange management course contains an experiment inspired by experiential teaching literature...... and methods. The experiment seeks to make students not only hear/learn about change agency and management, but to make them feel cha nge, hereby enabling them to develop the skills and competencies necessary for them to take on the role as change agent s and thus enable them to play key role s in implementing...

Agent-Based Optimization

CERN Document Server

Jędrzejowicz, Piotr; Kacprzyk, Janusz

2013-01-01

This volume presents a collection of original research works by leading specialists focusing on novel and promising approaches in which the multi-agent system paradigm is used to support, enhance or replace traditional approaches to solving difficult optimization problems. The editors have invited several well-known specialists to present their solutions, tools, and models falling under the common denominator of the agent-based optimization. The book consists of eight chapters covering examples of application of the multi-agent paradigm and respective customized tools to solve  difficult optimization problems arising in different areas such as machine learning, scheduling, transportation and, more generally, distributed and cooperative problem solving.

Face-to-Face Interaction with Pedagogical Agents, Twenty Years Later

Science.gov (United States)

Johnson, W. Lewis; Lester, James C.

2016-01-01

Johnson et al. ("International Journal of Artificial Intelligence in Education," 11, 47-78, 2000) introduced and surveyed a new paradigm for interactive learning environments: animated pedagogical agents. The article argued for combining animated interface agent technologies with intelligent learning environments, yielding intelligent…

The Role of Implicit Motives in Strategic Decision-Making: Computational Models of Motivated Learning and the Evolution of Motivated Agents

Directory of Open Access Journals (Sweden)

Kathryn Merrick

2015-11-01

Full Text Available Individual behavioral differences in humans have been linked to measurable differences in their mental activities, including differences in their implicit motives. In humans, individual differences in the strength of motives such as power, achievement and affiliation have been shown to have a significant impact on behavior in social dilemma games and during other kinds of strategic interactions. This paper presents agent-based computational models of power-, achievement- and affiliation-motivated individuals engaged in game-play. The first model captures learning by motivated agents during strategic interactions. The second model captures the evolution of a society of motivated agents. It is demonstrated that misperception, when it is a result of motivation, causes agents with different motives to play a given game differently. When motivated agents who misperceive a game are present in a population, higher explicit payoff can result for the population as a whole. The implications of these results are discussed, both for modeling human behavior and for designing artificial agents with certain salient behavioral characteristics.

When expectation confounds iconic memory.

Science.gov (United States)

Bachmann, Talis; Aru, Jaan

2016-10-01

In response to the methodological criticism (Bachmann & Aru, 2015) of the interpretation of their earlier experimental results (Mack, Erol, & Clarke, 2015) Mack, Erol, Clarke, and Bert (2016) presented new results that they interpret again in favor of the stance that an attention-free phenomenal iconic store does not exist. Here we once more question their conclusions. When their subjects were unexpectedly asked to report the letters instead of the post-cued circles in the 101th trial where letters were actually absent, they likely failed to see the empty display area because prior experience with letters in the preceding trials produced expectancy based illusory experience of letter-like objects. Copyright © 2016 Elsevier Inc. All rights reserved.

The Effects of Social Cue Principles on Cognitive Load, Situational Interest, Motivation, and Achievement in Pedagogical Agent Multimedia Learning

Science.gov (United States)

Park, Sanghoon

2015-01-01

Animated pedagogical agents have become popular in multimedia learning with combined delivery of verbal and non-verbal forms of information. In order to reduce unnecessary cognitive load caused by such multiple forms of information and also to foster generative cognitive processing, multimedia design principles with social cues are suggested…

A Watershed-Scale Agent-Based Model Incorporating Agent Learning and Interaction of Farmers' Decisions Subject to Carbon and Miscanthus Prices

Science.gov (United States)

Ng, T.; Eheart, J.; Cai, X.; Braden, J. B.

2010-12-01

Agricultural watersheds are coupled human-natural systems where the land use decisions of human agents (farmers) affect surface water quality, and in turn, are affected by the weather and yields. The reliable modeling of such systems requires an approach that considers both the human and natural aspects. Agent-based modeling (ABM), representing the human aspect, coupled with hydrologic modeling, representing the natural aspect, is one such approach. ABM is a relatively new modeling paradigm that formulates the system from the perspectives of the individual agents, i.e., each agent is modeled as a discrete autonomous entity with distinct goals and actions. The primary objective of this study is to demonstrate the applicability of this approach to agricultural watershed management. This is done using a semi-hypothetical case study of farmers in the Salt Creek watershed in East-Central Illinois under the influence markets for carbon and second-generation bioenergy crop (specifically, miscanthus). An agent-based model of the system is developed and linked to a hydrologic model of the watershed. The former is based on fundamental economic and mathematical programming principles, while the latter is based on the Soil and Water Assessment Tool (SWAT). Carbon and second-generation bioenergy crop markets are of interest here due to climate change and energy independence concerns. The agent-based model is applied to fifty hypothetical heterogeneous farmers. The farmers' decisions depend on their perceptions of future conditions. Those perceptions are updated, according to a pre-defined algorithm, as the farmers make new observations of prices, costs, yields and the weather with time. The perceptions are also updated as the farmers interact with each other as they share new information on initially unfamiliar activities (e.g., carbon trading, miscanthus cultivation). The updating algorithm is set differently for different farmers such that each is unique in his processing of

Learning Multirobot Hose Transportation and Deployment by Distributed Round-Robin Q-Learning.

Directory of Open Access Journals (Sweden)

Borja Fernandez-Gauna

Full Text Available Multi-Agent Reinforcement Learning (MARL algorithms face two main difficulties: the curse of dimensionality, and environment non-stationarity due to the independent learning processes carried out by the agents concurrently. In this paper we formalize and prove the convergence of a Distributed Round Robin Q-learning (D-RR-QL algorithm for cooperative systems. The computational complexity of this algorithm increases linearly with the number of agents. Moreover, it eliminates environment non sta tionarity by carrying a round-robin scheduling of the action selection and execution. That this learning scheme allows the implementation of Modular State-Action Vetoes (MSAV in cooperative multi-agent systems, which speeds up learning convergence in over-constrained systems by vetoing state-action pairs which lead to undesired termination states (UTS in the relevant state-action subspace. Each agent's local state-action value function learning is an independent process, including the MSAV policies. Coordination of locally optimal policies to obtain the global optimal joint policy is achieved by a greedy selection procedure using message passing. We show that D-RR-QL improves over state-of-the-art approaches, such as Distributed Q-Learning, Team Q-Learning and Coordinated Reinforcement Learning in a paradigmatic Linked Multi-Component Robotic System (L-MCRS control problem: the hose transportation task. L-MCRS are over-constrained systems with many UTS induced by the interaction of the passive linking element and the active mobile robots.

Proposed Methodology for Application of Human-like gradual Multi-Agent Q-Learning (HuMAQ) for Multi-robot Exploration

International Nuclear Information System (INIS)

Ray, Dip Narayan; Majumder, Somajyoti

2014-01-01

Several attempts have been made by the researchers around the world to develop a number of autonomous exploration techniques for robots. But it has been always an important issue for developing the algorithm for unstructured and unknown environments. Human-like gradual Multi-agent Q-leaming (HuMAQ) is a technique developed for autonomous robotic exploration in unknown (and even unimaginable) environments. It has been successfully implemented in multi-agent single robotic system. HuMAQ uses the concept of Subsumption architecture, a well-known Behaviour-based architecture for prioritizing the agents of the multi-agent system and executes only the most common action out of all the different actions recommended by different agents. Instead of using new state-action table (Q-table) each time, HuMAQ uses the immediate past table for efficient and faster exploration. The proof of learning has also been established both theoretically and practically. HuMAQ has the potential to be used in different and difficult situations as well as applications. The same architecture has been modified to use for multi-robot exploration in an environment. Apart from all other existing agents used in the single robotic system, agents for inter-robot communication and coordination/ co-operation with the other similar robots have been introduced in the present research. Current work uses a series of indigenously developed identical autonomous robotic systems, communicating with each other through ZigBee protocol

Agent-Based Approach for E-Learning

Directory of Open Access Journals (Sweden)

Samir Bourekkache

2009-12-01

Full Text Available The current life knows a remarkable development which changes all aspects of our life such as: our working environment, educational system, traveling patterns, sport activities…etc. The busy lifestyle faced by individuals today and the fast pattern of life may lead to their inability to be involved in a process of education. There is also a considerable technological development, especially the development of computing and communication technology. All these factors led to the idea of the need to reduce the time and to benefit from this technology, particularly in the field of education. The way of the classical education is very slow, and the student is obliged to be present at specific times which may be inappropriate for the majority of learners. Moreover, the substantial funds allocated for the success of classical learning process. Thus, it emerged the so-called E-learning via the Internet; the aims of this new mode of learning across the network (web are to reduce the time of a process of learning –education- as well as to erase the drawbacks of the classical way of education. Therefore, in E-learning, the learner is not forced to be present at specific times, but he is free to choose the time of learning, which is appropriate with his schedule. Another point that is the adaptation of the content (courses with the intellectual and social characteristics of the learner and with his background (previous knowledge, which is the main task for each educational system. The technology of Multiagent system is relevant in this area.

Learning Companion Systems, Social Learning Systems, and the Global Social Learning Club.

Science.gov (United States)

Chan, Tak-Wai

1996-01-01

Describes the development of learning companion systems and their contributions to the class of social learning systems that integrate artificial intelligence agents and use machine learning to tutor and interact with students. Outlines initial social learning projects, their programming languages, and weakness. Future improvements will include…

Learning from Errors

OpenAIRE

Martínez-Legaz, Juan Enrique; Soubeyran, Antoine

2003-01-01

We present a model of learning in which agents learn from errors. If an action turns out to be an error, the agent rejects not only that action but also neighboring actions. We find that, keeping memory of his errors, under mild assumptions an acceptable solution is asymptotically reached. Moreover, one can take advantage of big errors for a faster learning.

Patterns of Use of an Agent-Based Model and a System Dynamics Model: The Application of Patterns of Use and the Impacts on Learning Outcomes

Science.gov (United States)

Thompson, Kate; Reimann, Peter

2010-01-01

A classification system that was developed for the use of agent-based models was applied to strategies used by school-aged students to interrogate an agent-based model and a system dynamics model. These were compared, and relationships between learning outcomes and the strategies used were also analysed. It was found that the classification system…

Building an Educational Program together health community agents

Directory of Open Access Journals (Sweden)

Lúcia Rondelo Duarte

2007-01-01

Full Text Available Aiming at contributing inputs to the learning process of community health agents from Family Health Strategy, this study has sought to devise an Educational Program to qualify seven community agents from the Family Health Unit on Habiteto, a neighborhood in the Brazilian city of Sorocaba. Speeches on the perception these agents have of their work, their difficulties and proposals were captured and analyzed within the framework of the "Collective Subject Speech". Results showed the group's learning needs, and guided the devising and implementation of the Educational Program, which adopted the "Problem-Based Education" model. This knowledge was built by the agents through a problem-focused reality, debating, searching for solutions, and implementing intervention projects. They noticed that being a community health agent means, above all, to struggle and harness community forces for purposes of defending health & education public services and for improving social health determinants.

Mean-field theory of meta-learning

International Nuclear Information System (INIS)

Plewczynski, Dariusz

2009-01-01

We discuss here the mean-field theory for a cellular automata model of meta-learning. Meta-learning is the process of combining outcomes of individual learning procedures in order to determine the final decision with higher accuracy than any single learning method. Our method is constructed from an ensemble of interacting, learning agents that acquire and process incoming information using various types, or different versions, of machine learning algorithms. The abstract learning space, where all agents are located, is constructed here using a fully connected model that couples all agents with random strength values. The cellular automata network simulates the higher level integration of information acquired from the independent learning trials. The final classification of incoming input data is therefore defined as the stationary state of the meta-learning system using simple majority rule, yet the minority clusters that share the opposite classification outcome can be observed in the system. Therefore, the probability of selecting a proper class for a given input data, can be estimated even without the prior knowledge of its affiliation. The fuzzy logic can be easily introduced into the system, even if learning agents are built from simple binary classification machine learning algorithms by calculating the percentage of agreeing agents

Aspects of agents for safeguards

International Nuclear Information System (INIS)

Kotte, U.

1999-01-01

With the development of the Internet and the WWW, information treatment has gained a new dimension. (Intelligent) software agents are one of the means expected to relieve human staff of the burden of information overload, and in the future to contribute to safeguards data acquisition, data evaluation and decision-making. An overview is given for the categories of Internet, intranet and desktop agents. Aspects of the potential application of agents are described in three fields: information access and delivery, collaboration and workflow management, adaptive interfaces and learning assistants. Routine application of agents is not yet in sight, but the scientific and technical progress seems to be encouraging. (author)

10th KES Conference on Agent and Multi-Agent Systems : Technologies and Applications

CERN Document Server

Chen-Burger, Yun-Heh; Howlett, Robert; Jain, Lakhmi

2016-01-01

The modern economy is driven by technologies and knowledge. Digital technologies can free, shift and multiply choices, often intruding on the space of other industries, by providing new ways of conducting business operations and creating values for customers and companies. The topics covered in this volume include software agents, multi-agent systems, agent modelling, mobile and cloud computing, big data analysis, business intelligence, artificial intelligence, social systems, computer embedded systems and nature inspired manufacturing, etc. that contribute to the modern Digital Economy. This volume highlights new trends and challenges in agent, new digital and knowledge economy research and includes 28 papers classified in the following specific topics: business process management, agent-based modeling and simulation, anthropic-oriented computing, learning paradigms, business informatics and gaming, digital economy, and advances in networked virtual enterprises. Published papers were selected for presentatio...

Reinforcement learning in supply chains.

Science.gov (United States)

Valluri, Annapurna; North, Michael J; Macal, Charles M

2009-10-01

Effective management of supply chains creates value and can strategically position companies. In practice, human beings have been found to be both surprisingly successful and disappointingly inept at managing supply chains. The related fields of cognitive psychology and artificial intelligence have postulated a variety of potential mechanisms to explain this behavior. One of the leading candidates is reinforcement learning. This paper applies agent-based modeling to investigate the comparative behavioral consequences of three simple reinforcement learning algorithms in a multi-stage supply chain. For the first time, our findings show that the specific algorithm that is employed can have dramatic effects on the results obtained. Reinforcement learning is found to be valuable in multi-stage supply chains with several learning agents, as independent agents can learn to coordinate their behavior. However, learning in multi-stage supply chains using these postulated approaches from cognitive psychology and artificial intelligence take extremely long time periods to achieve stability which raises questions about their ability to explain behavior in real supply chains. The fact that it takes thousands of periods for agents to learn in this simple multi-agent setting provides new evidence that real world decision makers are unlikely to be using strict reinforcement learning in practice.

Improving Disability Awareness among Extension Agents

Science.gov (United States)

Mahadevan, Lakshmi; Peterson, Rick L.; Grenwelge, Cheryl

2014-01-01

Increasing prevalence rates and legislative mandates imply that educators, parents, and Extension agents will need better tools and resources to meet the needs of special populations. The Texas A&M AgriLife Extension Service addresses this issue by using e-learning tools. Extension agents can take advantage of these courses to gain critical…

Using Cognitive Agents to Train Negotiation Skills

Directory of Open Access Journals (Sweden)

Christopher A. Stevens

2018-02-01

Full Text Available Training negotiation is difficult because it is a complex, dynamic activity that involves multiple parties. It is often not clear how to create situations in which students can practice negotiation or how to measure students' progress. Some have begun to address these issues by creating artificial software agents with which students can train. These agents have the advantage that they can be “reset,” and played against multiple times. This allows students to learn from their mistakes and try different strategies. However, these agents are often based on normative theories of how negotiators should conduct themselves, not necessarily how people actually behave in negotiations. Here, we take a step toward addressing this gap by developing an agent grounded in a cognitive architecture, ACT-R. This agent contains a model of theory-of-mind, the ability of humans to reason about the mental states of others. It uses this model to try to infer the strategy of the opponent and respond accordingly. In a series of experiments, we show that this agent replicates some aspects of human performance, is plausible to human negotiators, and can lead to learning gains in a small-scale negotiation task.

Using Cognitive Agents to Train Negotiation Skills.

Science.gov (United States)

Stevens, Christopher A; Daamen, Jeroen; Gaudrain, Emma; Renkema, Tom; Top, Jakob Dirk; Cnossen, Fokie; Taatgen, Niels A

2018-01-01

Training negotiation is difficult because it is a complex, dynamic activity that involves multiple parties. It is often not clear how to create situations in which students can practice negotiation or how to measure students' progress. Some have begun to address these issues by creating artificial software agents with which students can train. These agents have the advantage that they can be "reset," and played against multiple times. This allows students to learn from their mistakes and try different strategies. However, these agents are often based on normative theories of how negotiators should conduct themselves, not necessarily how people actually behave in negotiations. Here, we take a step toward addressing this gap by developing an agent grounded in a cognitive architecture, ACT-R. This agent contains a model of theory-of-mind, the ability of humans to reason about the mental states of others. It uses this model to try to infer the strategy of the opponent and respond accordingly. In a series of experiments, we show that this agent replicates some aspects of human performance, is plausible to human negotiators, and can lead to learning gains in a small-scale negotiation task.

Reverse engineering a social agent-based hidden markov model--visage.

Science.gov (United States)

Chen, Hung-Ching Justin; Goldberg, Mark; Magdon-Ismail, Malik; Wallace, William A

2008-12-01

We present a machine learning approach to discover the agent dynamics that drives the evolution of the social groups in a community. We set up the problem by introducing an agent-based hidden Markov model for the agent dynamics: an agent's actions are determined by micro-laws. Nonetheless, We learn the agent dynamics from the observed communications without knowing state transitions. Our approach is to identify the appropriate micro-laws corresponding to an identification of the appropriate parameters in the model. The model identification problem is then formulated as a mixed optimization problem. To solve the problem, we develop a multistage learning process for determining the group structure, the group evolution, and the micro-laws of a community based on the observed set of communications among actors, without knowing the semantic contents. Finally, to test the quality of our approximations and the feasibility of the approach, we present the results of extensive experiments on synthetic data as well as the results on real communities, such as Enron email and Movie newsgroups. Insight into agent dynamics helps us understand the driving forces behind social evolution.

Layered Learning in Multi-Agent Systems

Science.gov (United States)

1998-12-15

project almost from the beginning has tirelessly experimented with different robot architectures, always managing to pull things together and create...TEAM MEMBER AGENT ARCHITECTURE I " ! Midfielder, Left : • i ) ( ^ J Goalie , Center Home Coordinates Home Range Max Range Figure

An Immune Agent for Web-Based AI Course

Science.gov (United States)

Gong, Tao; Cai, Zixing

2006-01-01

To overcome weakness and faults of a web-based e-learning course such as Artificial Intelligence (AI), an immune agent was proposed, simulating a natural immune mechanism against a virus. The immune agent was built on the multi-dimension education agent model and immune algorithm. The web-based AI course was comprised of many files, such as HTML…

The Influence of a Pedagogical Agent on Learners' Cognitive Load

Science.gov (United States)

Schroeder, Noah L.

2017-01-01

According to cognitive load theorists, the incorporation of extraneous features, such as pedagogical agents, into the learning environment can introduce extraneous cognitive load and thus interfere with learning outcome scores. In this study, the influence of a pedagogical agent's presence in an instructional video was compared to a video that did…

Multi-Agent Inference in Social Networks: A Finite Population Learning Approach.

Science.gov (United States)

Fan, Jianqing; Tong, Xin; Zeng, Yao

When people in a society want to make inference about some parameter, each person may want to use data collected by other people. Information (data) exchange in social networks is usually costly, so to make reliable statistical decisions, people need to trade off the benefits and costs of information acquisition. Conflicts of interests and coordination problems will arise in the process. Classical statistics does not consider people's incentives and interactions in the data collection process. To address this imperfection, this work explores multi-agent Bayesian inference problems with a game theoretic social network model. Motivated by our interest in aggregate inference at the societal level, we propose a new concept, finite population learning , to address whether with high probability, a large fraction of people in a given finite population network can make "good" inference. Serving as a foundation, this concept enables us to study the long run trend of aggregate inference quality as population grows.

2015 Special Sessions of the 13th International Conference on Practical Applications of Agents and Multi-Agent Systems

CERN Document Server

Hernández, Josefa; Mathieu, Philippe; Campbell, Andrew; Fernández-Caballero, Antonio; Moreno, María; Julián, Vicente; Alonso-Betanzos, Amparo; Jiménez-López, María; Botti, Vicente; Trends in Practical Applications of Agents, Multi-Agent Systems and Sustainability : the PAAMS Collection

2015-01-01

This volume presents the papers that have been accepted for the 2015 special sessions of the 13th International Conference on Practical Applications of Agents and Multi-Agent Systems, held at University of Salamanca, Spain, at 3rd-5th June, 2015: Agents Behaviours and Artificial Markets (ABAM); Agents and Mobile Devices (AM); Multi-Agent Systems and Ambient Intelligence (MASMAI); Web Mining and Recommender systems (WebMiRes); Learning, Agents and Formal Languages (LAFLang); Agent-based Modeling of Sustainable Behavior and Green Economies (AMSBGE); Emotional Software Agents (SSESA) and Intelligent Educational Systems (SSIES). The volume also includes the paper accepted for the Doctoral Consortium in PAAMS 2015. PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an evolution of the International Workshop on Practical Applications of Agents and Multi-Agent Systems. PAAMS is an international yearly tribune to present, to discuss, and to disseminate the latest develo...

Emergence of heterogeneity in an agent-based model

OpenAIRE

Abdullah, Wan Ahmad Tajuddin Wan

2002-01-01

We study an interacting agent model of a game-theoretical economy. The agents play a minority-subsequently-majority game and they learn, using backpropagation networks, to obtain higher payoffs. We study the relevance of heterogeneity to performance, and how heterogeneity emerges.

An Online Q-learning Based Multi-Agent LFC for a Multi-Area Multi-Source Power System Including Distributed Energy Resources

Directory of Open Access Journals (Sweden)

H. Shayeghi

2017-12-01

Full Text Available This paper presents an online two-stage Q-learning based multi-agent (MA controller for load frequency control (LFC in an interconnected multi-area multi-source power system integrated with distributed energy resources (DERs. The proposed control strategy consists of two stages. The first stage is employed a PID controller which its parameters are designed using sine cosine optimization (SCO algorithm and are fixed. The second one is a reinforcement learning (RL based supplementary controller that has a flexible structure and improves the output of the first stage adaptively based on the system dynamical behavior. Due to the use of RL paradigm integrated with PID controller in this strategy, it is called RL-PID controller. The primary motivation for the integration of RL technique with PID controller is to make the existing local controllers in the industry compatible to reduce the control efforts and system costs. This novel control strategy combines the advantages of the PID controller with adaptive behavior of MA to achieve the desired level of robust performance under different kind of uncertainties caused by stochastically power generation of DERs, plant operational condition changes, and physical nonlinearities of the system. The suggested decentralized controller is composed of the autonomous intelligent agents, who learn the optimal control policy from interaction with the system. These agents update their knowledge about the system dynamics continuously to achieve a good frequency oscillation damping under various severe disturbances without any knowledge of them. It leads to an adaptive control structure to solve LFC problem in the multi-source power system with stochastic DERs. The results of RL-PID controller in comparison to the traditional PID and fuzzy-PID controllers is verified in a multi-area power system integrated with DERs through some performance indices.

Facilitating Learning Organizations. Making Learning Count.

Science.gov (United States)

Marsick, Victoria J.; Watkins, Karen E.

This book offers advice to facilitators and change agents who wish to build systems-level learning to create knowledge that can be used to gain a competitive advantage. Chapter 1 describes forces driving companies to build, sustain, and effectively use systems-level learning and presents and links a working definition of the learning organization…

Incremental learning of skill collections based on intrinsic motivation

Science.gov (United States)

Metzen, Jan H.; Kirchner, Frank

2013-01-01

Life-long learning of reusable, versatile skills is a key prerequisite for embodied agents that act in a complex, dynamic environment and are faced with different tasks over their lifetime. We address the question of how an agent can learn useful skills efficiently during a developmental period, i.e., when no task is imposed on him and no external reward signal is provided. Learning of skills in a developmental period needs to be incremental and self-motivated. We propose a new incremental, task-independent skill discovery approach that is suited for continuous domains. Furthermore, the agent learns specific skills based on intrinsic motivation mechanisms that determine on which skills learning is focused at a given point in time. We evaluate the approach in a reinforcement learning setup in two continuous domains with complex dynamics. We show that an intrinsically motivated, skill learning agent outperforms an agent which learns task solutions from scratch. Furthermore, we compare different intrinsic motivation mechanisms and how efficiently they make use of the agent's developmental period. PMID:23898265

Multi-agent sequential hypothesis testing

KAUST Repository

Kim, Kwang-Ki K.

2014-12-15

This paper considers multi-agent sequential hypothesis testing and presents a framework for strategic learning in sequential games with explicit consideration of both temporal and spatial coordination. The associated Bayes risk functions explicitly incorporate costs of taking private/public measurements, costs of time-difference and disagreement in actions of agents, and costs of false declaration/choices in the sequential hypothesis testing. The corresponding sequential decision processes have well-defined value functions with respect to (a) the belief states for the case of conditional independent private noisy measurements that are also assumed to be independent identically distributed over time, and (b) the information states for the case of correlated private noisy measurements. A sequential investment game of strategic coordination and delay is also discussed as an application of the proposed strategic learning rules.

Data Mining Process Optimization in Computational Multi-agent Systems

OpenAIRE

Kazík, O.; Neruda, R. (Roman)

2015-01-01

In this paper, we present an agent-based solution of metalearning problem which focuses on optimization of data mining processes. We exploit the framework of computational multi-agent systems in which various meta-learning problems have been already studied, e.g. parameter-space search or simple method recommendation. In this paper, we examine the effect of data preprocessing for machine learning problems. We perform the set of experiments in the search-space of data mining processes which is...

Disainikaart : Türgi / Silvia Pärmann

Index Scriptorium Estoniae

Pärmann, Silvia

2009-01-01

Türgi disaineritest, disainifirmadest, arhitektidest. Disainerid ja arhitektid Fevzi Karaman, Nedret ja Mark Butler, Defne Koz (sünd. 1964), Aziz Sariyer, Aykut Erol. Firmad GAEAforms, MayBeDesign, Autoban, Derin Design, Stepevi, Gaia & Gino

Practice among Novice Change Agents in Schools

Science.gov (United States)

Blossing, Ulf

2016-01-01

The aim of the article is to understand practice as negotiation of meaning among novice and internal change agents in school organisations. The research question is as follows: What themes of participation and reification/management occur among the change agents? The study was qualitative in design using the social learning theory of community of…

Reinforcement Learning in the Game of Othello: Learning Against a Fixed Opponent and Learning from Self-Play

NARCIS (Netherlands)

van der Ree, Michiel; Wiering, Marco

2013-01-01

This paper compares three strategies in using reinforcement learning algorithms to let an artificial agent learnto play the game of Othello. The three strategies that are compared are: Learning by self-play, learning from playing against a fixed opponent, and learning from playing against a fixed

Socially intelligent autonomous agents that learn from human reward

NARCIS (Netherlands)

Li, Guangliang

2016-01-01

In the future, autonomous agents will operate in human inhabited environments in many real world applications and become an integral part of human’s daily lives. Therefore, when autonomous agents enter into the real world, they need to adapt to many novel, dynamic and complex situations that cannot

Change Agents and Collective Experience- Making as Part of ...

African Journals Online (AJOL)

agents can enable and facilitate collective learning about climate change, as well as ... transcend such approaches through the development of longer-term social .... 'acquisition of mediated experiences in the learning rhythm of the immediate ...

Change Agents and Collective Experience-Making as Part of ...

African Journals Online (AJOL)

agents can enable and facilitate collective learning about climate change, as well ... interconnected global problems, meaning that efforts to reduce and adapt to ..... 'acquisition of mediated experiences in the learning rhythm of the immediate ...

Agent-based modeling of sustainable behaviors

CERN Document Server

Sánchez-Maroño, Noelia; Fontenla-Romero, Oscar; Polhill, J; Craig, Tony; Bajo, Javier; Corchado, Juan

2017-01-01

Using the O.D.D. (Overview, Design concepts, Detail) protocol, this title explores the role of agent-based modeling in predicting the feasibility of various approaches to sustainability. The chapters incorporated in this volume consist of real case studies to illustrate the utility of agent-based modeling and complexity theory in discovering a path to more efficient and sustainable lifestyles. The topics covered within include: households' attitudes toward recycling, designing decision trees for representing sustainable behaviors, negotiation-based parking allocation, auction-based traffic signal control, and others. This selection of papers will be of interest to social scientists who wish to learn more about agent-based modeling as well as experts in the field of agent-based modeling.

Adaptive representations for reinforcement learning

NARCIS (Netherlands)

Whiteson, S.

2010-01-01

This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own

An agent-based approach with collaboration among agents. Estimation of wholesale electricity price on PJM and artificial data generated by a mean reverting model

International Nuclear Information System (INIS)

Sueyoshi, Toshiyuki

2010-01-01

This study examines the performance of MAIS (Multi-Agent Intelligent Simulator) equipped with various learning capabilities. In addition to the learning capabilities, the proposed MAIS incorporates collaboration among agents. The proposed MAIS is applied to estimate a dynamic change of wholesale electricity price in PJM (Pennsylvania-New Jersey-Mainland) and an artificial data set generated by a mean reverting model. Using such different types of data sets, the methodological validity of MAIS is confirmed by comparing it with other well-known alternatives in computer science. This study finds that the MAIS needs to incorporate both the mean reverting model and the collaboration behavior among agents in order to enhance its estimation capability. The MAIS discussed in this study will provide research on energy economics with a new numerical capability that can investigate a dynamic change of not only wholesale electricity price but also speculation and learning process of traders. (author)

Building an adaptive agent to monitor and repair the electrical power system of an orbital satellite

Science.gov (United States)

Tecuci, Gheorghe; Hieb, Michael R.; Dybala, Tomasz

1995-01-01

Over several years we have developed a multistrategy apprenticeship learning methodology for building knowledge-based systems. Recently we have developed and applied our methodology to building intelligent agents. This methodology allows a subject matter expert to build an agent in the same way in which the expert would teach a human apprentice. The expert will give the agent specific examples of problems and solutions, explanations of these solutions, or supervise the agent as it solves new problems. During such interactions, the agent learns general rules and concepts, continuously extending and improving its knowledge base. In this paper we present initial results on applying this methodology to build an intelligent adaptive agent for monitoring and repair of the electrical power system of an orbital satellite, stressing the interaction with the expert during apprenticeship learning.

Multi-Agent Design and Implementation for an Online Peer Help System

Science.gov (United States)

Meng, Anbo

2014-01-01

With the rapid advance of e-learning, the online peer help is playing increasingly important role. This paper explores the application of MAS to an online peer help system (MAPS). In the design phase, the architecture of MAPS is proposed, which consists of a set of agents including the personal agent, the course agent, the diagnosis agent, the DF…

Conversational agents for academically productive talk: a comparison of directed and undirected agent interventions

DEFF Research Database (Denmark)

Tegos, Stergios; Demetriadis, Stavros N.; Papadopoulos, Pantelis M.

2016-01-01

Conversational agents that draw on the framework of academically productive talk (APT) have been lately shown to be effective in helping learners sustain productive forms of peer dialogue in diverse learning settings. Yet, literature suggests that more research is required on how learners respond...

A Cognitive Agent for Spectrum Monitoring and Informed Spectrum Access

Science.gov (United States)

2017-06-01

sensing, analysis, learning, short - and long - term memory , problem solving, decision making, and focus of attention. The goal of the cognitive agent was to...Level 2: Analyzing 2 3.3 DOK Level 3: Learning and Short - and Long - Term Memories 5 3.4 DOK Level 4: Focus of Attention, Problem Solving, and... short - and long - term memory , problem solving, decision making, and focus of attention.1 The goal of the cognitive agent was to mimic intelligent

Autonomous parsing of behavior in a multi-agent setting

NARCIS (Netherlands)

Vanderelst, D.; Barakova, E.I.; Rutkowski, L.; Tadeusiewicz, R.

2008-01-01

Imitation learning is a promising route to instruct robotic multi-agent systems. However, imitating agents should be able to decide autonomously what behavior, observed in others, is interesting to copy. Here we investigate whether a simple recurrent network (Elman Net) can be used to extract

Chronic Heart Failure Follow-up Management Based on Agent Technology.

Science.gov (United States)

Mohammadzadeh, Niloofar; Safdari, Reza

2015-10-01

Monitoring heart failure patients through continues assessment of sign and symptoms by information technology tools lead to large reduction in re-hospitalization. Agent technology is one of the strongest artificial intelligence areas; therefore, it can be expected to facilitate, accelerate, and improve health services especially in home care and telemedicine. The aim of this article is to provide an agent-based model for chronic heart failure (CHF) follow-up management. This research was performed in 2013-2014 to determine appropriate scenarios and the data required to monitor and follow-up CHF patients, and then an agent-based model was designed. Agents in the proposed model perform the following tasks: medical data access, communication with other agents of the framework and intelligent data analysis, including medical data processing, reasoning, negotiation for decision-making, and learning capabilities. The proposed multi-agent system has ability to learn and thus improve itself. Implementation of this model with more and various interval times at a broader level could achieve better results. The proposed multi-agent system is no substitute for cardiologists, but it could assist them in decision-making.

Multidimensional (OLAP) Analysis for Designing Dynamic Learning Strategy

Science.gov (United States)

Rozeva, A.; Deliyska, B.

2010-10-01

Learning strategy in an intelligent learning system is generally elaborated on the basis of assessment of the following factors: learner's time for reaction, content of the learning object, amount of learning material in a learning object, learning object specification, e-learning medium and performance control. Current work proposes architecture for dynamic learning strategy design by implementing multidimensional analysis model of learning factors. The analysis model concerns on-line analytical processing (OLAP) of learner's data structured as multidimensional cube. Main components of the architecture are analysis agent for performing the OLAP operations on learner data cube, adaptation generator and knowledge selection agent for performing adaptive navigation in the learning object repository. The output of the analysis agent is involved in dynamic elaboration of learning strategy that fits best to learners profile and behavior. As a result an adaptive learning path for individual learner and for learner groups is generated.

Connectionist agent-based learning in bank-run decision making

Science.gov (United States)

Huang, Weihong; Huang, Qiao

2018-05-01

It is of utter importance for the policy makers, bankers, and investors to thoroughly understand the probability of bank-run (PBR) which was often neglected in the classical models. Bank-run is not merely due to miscoordination (Diamond and Dybvig, 1983) or deterioration of bank assets (Allen and Gale, 1998) but various factors. This paper presents the simulation results of the nonlinear dynamic probabilities of bank runs based on the global games approach, with the distinct assumption that heterogenous agents hold highly correlated but unidentical beliefs about the true payoffs. The specific technique used in the simulation is to let agents have an integrated cognitive-affective network. It is observed that, even when the economy is good, agents are significantly affected by the cognitive-affective network to react to bad news which might lead to bank-run. Both the rise of the late payoffs, R, and the early payoffs, r, will decrease the effect of the affective process. The increased risk sharing might or might not increase PBR, and the increase in late payoff is beneficial for preventing the bank run. This paper is one of the pioneers that links agent-based computational economics and behavioral economics.

Learning via Query Synthesis

KAUST Repository

Alabdulmohsin, Ibrahim

2017-01-01

Active learning is a subfield of machine learning that has been successfully used in many applications. One of the main branches of active learning is query synthe- sis, where the learning agent constructs artificial queries from scratch in order

Human-level control through deep reinforcement learning

Science.gov (United States)

Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A.; Veness, Joel; Bellemare, Marc G.; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K.; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

2015-02-01

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

Human-level control through deep reinforcement learning.

Science.gov (United States)

Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A; Veness, Joel; Bellemare, Marc G; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

2015-02-26

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

Incremental Learning of Skill Collections based on Intrinsic Motivation

Directory of Open Access Journals (Sweden)

Jan Hendrik Metzen

2013-07-01

Full Text Available Life-long learning of reusable, versatile skills is a key prerequisite forembodied agents that act in a complex, dynamic environment and are faced withdifferent tasks over their lifetime. We address the question of how an agentcan learn useful skills efficiently during a developmental period,i.e., when no task is imposed on him and no external reward signal is provided.Learning of skills in a developmental period needs to be incremental andself-motivated. We propose a new incremental, task-independent skill discoveryapproach that is suited for continuous domains. Furthermore, the agent learnsspecific skills based on intrinsic motivation mechanisms thatdetermine on which skills learning is focused at a given point in time. Weevaluate the approach in a reinforcement learning setup in two continuousdomains with complex dynamics. We show that an intrinsically motivated, skilllearning agent outperforms an agent which learns task solutions from scratch.Furthermore, we compare different intrinsic motivation mechanisms and howefficiently they make use of the agent's developmental period.

Intelligent virtual agents as language trainers facilitate multilingualism.

Science.gov (United States)

Macedonia, Manuela; Groher, Iris; Roithmayr, Friedrich

2014-01-01

intelligent virtual agents (IVAs) with human appearance and the capability to teach foreign language vocabulary. We report results from studies that we have conducted with Billie, an IVA employed as a vocabulary trainer, as well as research findings on the acceptance of the agent as a trainer by adults and children. The results show that Billie can train humans as well as a human teacher can and that both adults and children accept the IVA as a trainer. The advantages of IVAs are multiple. First, their teaching methods can be based on neuropsychological research findings concerning memory and learning practice. Second, virtual teachers can provide individualized training. Third, they coach users during training, are always supportive, and motivate learners to train. Fourth, agents will reside in the user's mobile devices and thus be at the user's disposal everywhere and anytime. Agents in apps will make foreign language training accessible to anybody at low cost. This will enable people around the world, including physically, financially, and geographically disadvantaged persons, to learn a foreign language and help to facilitate multilingualism.

Effects of repeated administration of chemotherapeutic agents tamoxifen, methotrexate, and 5-fluorouracil on the acquisition and retention of a learned response in mice

Science.gov (United States)

Foley, John J.; Clark-Vetri, Rachel; Raffa, Robert B.

2011-01-01

Rationale A number of cancer chemotherapeutic agents have been associated with a loss of memory in breast cancer patients although little is known of the causality of this effect. Objectives To assess the potential cognitive effects of repeated exposure to chemotherapeutic agents, we administered the selective estrogen receptor modulator tamoxifen or the antimetabolite chemotherapy, methotrexate, and 5-fluorouracil, alone and in combination to mice and tested them in a learning and memory assay. Methods Swiss-Webster male mice were injected with saline, 32 mg/kg tamoxifen, 3.2 or 32 mg/kg methotrexate, 75 mg/kg 5-fluorouracil, 3.2 or 32 mg/kg methotrexate in combination with 75 mg/kg 5-fluorouracil once per week for 3 weeks. On days 23 and 24, mice were tested for acquisition and retention of a nose-poke response in a learning procedure called autoshaping. In addition, the acute effects of tamoxifen were assessed in additional mice in a similar procedure. Results The chemotherapeutic agents alone and in combination reduced body weight relative to saline treatment over the course of 4 weeks. Repeated treatment with tamoxifen produced both acquisition and retention effects relative to the saline-treated group although acute tamoxifen was without effect except at a behaviorally toxic dose. Repeated treatment with methotrexate in combination with 5-fluorouracil produced effects on retention, but the magnitude of these changes depended on the methotrexate dose. Conclusions These data demonstrate that repeated administration of tamoxifen or certain combination of methotrexate and 5-fluorouracil may produce deficits in the acquisition or retention of learned responses which suggest potential strategies for prevention or remediation might be considered in vulnerable patient populations. PMID:21537942

FY1995 distributed control of man-machine cooperative multi agent systems; 1995 nendo ningen kyochogata multi agent kikai system no jiritsu seigyo

Energy Technology Data Exchange (ETDEWEB)

NONE

1997-03-01

In the near future, distributed autonomous systems will be practical in many situations, e.g., interactive production systems, hazardous environments, nursing homes, and individual houses. The agents which consist of the distributed system must not give damages to human being and should be working economically. In this project man-machine cooperative multi agent systems are studied in many kind of respects, and basic design technology, basic control technique are developed by establishing fundamental theories and by constructing experimental systems. In this project theoretical and experimental studies are conducted in the following sub-projects: (1) Distributed cooperative control in multi agent type actuation systems (2) Control of non-holonomic systems (3) Man-machine Cooperative systems (4) Robot systems learning human skills (5) Robust force control of constrained systems In each sub-project cooperative nature between machine agent systems and human being, interference between artificial multi agents and environment and new function emergence in coordination of the multi agents and the environment, robust force control against for the environments, control methods for non-holonomic systems, robot systems which can mimic and learn human skills were studied. In each sub-project, some problems were hi-lighted and solutions for the problems have been given based on construction of experimental systems. (NEDO)

Intelligent judgements over health risks in a spatial agent-based model.

Science.gov (United States)

Abdulkareem, Shaheen A; Augustijn, Ellen-Wien; Mustafa, Yaseen T; Filatova, Tatiana

2018-03-20

Millions of people worldwide are exposed to deadly infectious diseases on a regular basis. Breaking news of the Zika outbreak for instance, made it to the main media titles internationally. Perceiving disease risks motivate people to adapt their behavior toward a safer and more protective lifestyle. Computational science is instrumental in exploring patterns of disease spread emerging from many individual decisions and interactions among agents and their environment by means of agent-based models. Yet, current disease models rarely consider simulating dynamics in risk perception and its impact on the adaptive protective behavior. Social sciences offer insights into individual risk perception and corresponding protective actions, while machine learning provides algorithms and methods to capture these learning processes. This article presents an innovative approach to extend agent-based disease models by capturing behavioral aspects of decision-making in a risky context using machine learning techniques. We illustrate it with a case of cholera in Kumasi, Ghana, accounting for spatial and social risk factors that affect intelligent behavior and corresponding disease incidents. The results of computational experiments comparing intelligent with zero-intelligent representations of agents in a spatial disease agent-based model are discussed. We present a spatial disease agent-based model (ABM) with agents' behavior grounded in Protection Motivation Theory. Spatial and temporal patterns of disease diffusion among zero-intelligent agents are compared to those produced by a population of intelligent agents. Two Bayesian Networks (BNs) designed and coded using R and are further integrated with the NetLogo-based Cholera ABM. The first is a one-tier BN1 (only risk perception), the second is a two-tier BN2 (risk and coping behavior). We run three experiments (zero-intelligent agents, BN1 intelligence and BN2 intelligence) and report the results per experiment in terms of

Domain learning naming game for color categorization.

Science.gov (United States)

Li, Doujie; Fan, Zhongyan; Tang, Wallace K S

2017-01-01

Naming game simulates the evolution of vocabulary in a population of agents. Through pairwise interactions in the games, agents acquire a set of vocabulary in their memory for object naming. The existing model confines to a one-to-one mapping between a name and an object. Focus is usually put onto name consensus in the population rather than knowledge learning in agents, and hence simple learning model is usually adopted. However, the cognition system of human being is much more complex and knowledge is usually presented in a complicated form. Therefore, in this work, we extend the agent learning model and design a new game to incorporate domain learning, which is essential for more complicated form of knowledge. In particular, we demonstrate the evolution of color categorization and naming in a population of agents. We incorporate the human perceptive model into the agents and introduce two new concepts, namely subjective perception and subliminal stimulation, in domain learning. Simulation results show that, even without any supervision or pre-requisition, a consensus of a color naming system can be reached in a population solely via the interactions. Our work confirms the importance of society interactions in color categorization, which is a long debate topic in human cognition. Moreover, our work also demonstrates the possibility of cognitive system development in autonomous intelligent agents.

Action observation and robotic agents: learning and anthropomorphism.

Science.gov (United States)

Press, Clare

2011-05-01

The 'action observation network' (AON), which is thought to translate observed actions into motor codes required for their execution, is biologically tuned: it responds more to observation of human, than non-human, movement. This biological specificity has been taken to support the hypothesis that the AON underlies various social functions, such as theory of mind and action understanding, and that, when it is active during observation of non-human agents like humanoid robots, it is a sign of ascription of human mental states to these agents. This review will outline evidence for biological tuning in the AON, examining the features which generate it, and concluding that there is evidence for tuning to both the form and kinematic profile of observed movements, and little evidence for tuning to belief about stimulus identity. It will propose that a likely reason for biological tuning is that human actions, relative to non-biological movements, have been observed more frequently while executing corresponding actions. If the associative hypothesis of the AON is correct, and the network indeed supports social functioning, sensorimotor experience with non-human agents may help us to predict, and therefore interpret, their movements. Copyright © 2011 Elsevier Ltd. All rights reserved.

Decentralized Reinforcement Learning of robot behaviors

NARCIS (Netherlands)

Leottau, David L.; Ruiz-del-Solar, Javier; Babuska, R.

2018-01-01

A multi-agent methodology is proposed for Decentralized Reinforcement Learning (DRL) of individual behaviors in problems where multi-dimensional action spaces are involved. When using this methodology, sub-tasks are learned in parallel by individual agents working toward a common goal. In

Exploring e-learning knowledge through ontological memetic agents

NARCIS (Netherlands)

Acampora, G.; Gaeta, M.; Loia, V.

2010-01-01

E-Learning systems have proven to be fundamental in several areas of tertiary education and in business companies. There are many significant advantages for people who learn online such as convenience, portability, flexibility and costs. However, the remarkable velocity and volatility of modern

Effect of reinforcement learning on coordination of multiangent systems

Science.gov (United States)

Bukkapatnam, Satish T. S.; Gao, Greg

2000-12-01

For effective coordination of distributed environments involving multiagent systems, learning ability of each agent in the environment plays a crucial role. In this paper, we develop a simple group learning method based on reinforcement, and study its effect on coordination through application to a supply chain procurement scenario involving a computer manufacturer. Here, all parties are represented by self-interested, autonomous agents, each capable of performing specific simple tasks. They negotiate with each other to perform complex tasks and thus coordinate supply chain procurement. Reinforcement learning is intended to enable each agent to reach a best negotiable price within a shortest possible time. Our simulations of the application scenario under different learning strategies reveals the positive effects of reinforcement learning on an agent's as well as the system's performance.

Agent-Based Models in Social Physics

Science.gov (United States)

Quang, Le Anh; Jung, Nam; Cho, Eun Sung; Choi, Jae Han; Lee, Jae Woo

2018-06-01

We review the agent-based models (ABM) on social physics including econophysics. The ABM consists of agent, system space, and external environment. The agent is autonomous and decides his/her behavior by interacting with the neighbors or the external environment with the rules of behavior. Agents are irrational because they have only limited information when they make decisions. They adapt using learning from past memories. Agents have various attributes and are heterogeneous. ABM is a non-equilibrium complex system that exhibits various emergence phenomena. The social complexity ABM describes human behavioral characteristics. In ABMs of econophysics, we introduce the Sugarscape model and the artificial market models. We review minority games and majority games in ABMs of game theory. Social flow ABM introduces crowding, evacuation, traffic congestion, and pedestrian dynamics. We also review ABM for opinion dynamics and voter model. We discuss features and advantages and disadvantages of Netlogo, Repast, Swarm, and Mason, which are representative platforms for implementing ABM.

Analysis of Foreign Exchange Interventions by Intervention Agent with an Artificial Market Approach

Science.gov (United States)

Matsui, Hiroki; Tojo, Satoshi

We propose a multi-agent system which learns intervention policies and evaluates the effect of interventions in an artificial foreign exchange market. Izumi et al. had presented a system called AGEDASI TOF to simulate artificial market, together with a support system for the government to decide foreign exchange policies. However, the system needed to fix the amount of governmental intervention prior to the simulation, and was not realistic. In addition, the interventions in the system did not affect supply and demand of currencies; thus we could not discuss the effect of intervention correctly. First, we improve the system so as to make much of the weights of influential factors. Thereafter, we introduce an intervention agent that has the role of the central bank to stabilize the market. We could show that the agent learned the effective intervention policies through the reinforcement learning, and that the exchange rate converged to a certain extent in the expected range. We could also estimate the amount of intervention, showing the efficacy of signaling. In this model, in order to investigate the aliasing of the perception of the intervention agent, we introduced a pseudo-agent who was supposed to be able to observe all the behaviors of dealer agents; with this super-agent, we discussed the adequate granularity for a market state description.

Intelligent Virtual Agents as Language Trainers Facilitate Multilingualism

Directory of Open Access Journals (Sweden)

Manuela eMacedonia

2014-04-01

Full Text Available In this paper we introduce a new generation of language trainers: intelligent virtual agents (IVAs with human appearance and the capability to teach foreign language vocabulary. We report results from studies that we have conducted with Billie, an IVA employed as a vocabulary trainer, as well as research findings on the acceptance of the agent as a trainer by adults and children. The results show that Billie can train humans as well as a human teacher can and that both adults and children accept the IVA as a trainer. The advantages of IVAs are multiple. First, their teaching methods can be based on neuropsychological research findings concerning memory and learning practice. Second, virtual teachers can provide individualized training. Third, they coach users during training, are always supportive, and motivate learners to train. Fourth, agents will reside in the user’s mobile devices and thus be at the user’s disposal everywhere and anytime. Agents in apps will make foreign language training accessible to anybody at low cost. This will enable people around the world, including physically, financially and geographically disadvantaged persons, to learn a foreign language and help to facilitate multilingualism.

Nicholas Gilroy | NREL

Science.gov (United States)

Nicholas Gilroy desc Nicholas Gilroy Geospatial Data Scientist II Nicholas.Gilroy@nrel.gov | 303 -384-7354 Nicholas Gilroy is a member of the Geospatial Data Science team within the Systems Modeling (AAG) Featured Publications Veda, Santosh, Zhang, Yingchen, Tan, Jin, Chartan, Erol Kevin, Gilroy

Reply to Bachmann and Aru.

Science.gov (United States)

Mack, Arien; Clarke, Jason; Erol, Muge

2015-09-01

A reply to the Bachmann and Aru (2015) critique of our paper (Mack, Erol, & Clarke, 2015) in which we rebut their criticisms and argue once again that our results support our view that iconic memory requires attention. Copyright © 2015 Elsevier Inc. All rights reserved.

Measurement and Evaluation of Animated Pedagogical Agents and Their Use in Training

National Research Council Canada - National Science Library

Hosie, Thomas

2004-01-01

... on-screen agents that speak in a human voice rather than a machine-synthesized voice; (b) an image effect, an agent's image fosters learning when it is programmed to explain complex visual information aurally; (c...

Holograms as Teaching Agents

Science.gov (United States)

Walker, Robin A.

2013-02-01

Hungarian physicist Dennis Gabor won the Pulitzer Prize for his 1947 introduction of basic holographic principles, but it was not until the invention of the laser in 1960 that research scientists, physicians, technologists and the general public began to seriously consider the interdisciplinary potentiality of holography. Questions around whether and when Three-Dimensional (3-D) images and systems would impact American entertainment and the arts would be answered before educators, instructional designers and students would discover how much Three-Dimensional Hologram Technology (3DHT) would affect teaching practices and learning environments. In the following International Symposium on Display Holograms (ISDH) poster presentation, the author features a traditional board game as well as a reflection hologram to illustrate conventional and evolving Three-Dimensional representations and technology for education. Using elements from the American children's toy Operation® (Hasbro, 2005) as well as a reflection hologram of a human brain (Ko, 1998), this poster design highlights the pedagogical effects of 3-D images, games and systems on learning science. As teaching agents, holograms can be considered substitutes for real objects, (human beings, organs, and animated characters) as well as agents (pedagogical, avatars, reflective) in various learning environments using many systems (direct, emergent, augmented reality) and electronic tools (cellphones, computers, tablets, television). In order to understand the particular importance of utilizing holography in school, clinical and public settings, the author identifies advantages and benefits of using 3-D images and technology as instructional tools.

Multiagent cooperation and competition with deep reinforcement learning.

Directory of Open Access Journals (Sweden)

Ardi Tampuu

Full Text Available Evolution of cooperation and competition can appear when multiple adaptive agents share a biological, social, or technological niche. In the present work we study how cooperation and competition emerge between autonomous agents that learn by reinforcement while using only their raw visual input as the state representation. In particular, we extend the Deep Q-Learning framework to multiagent environments to investigate the interaction between two learning agents in the well-known video game Pong. By manipulating the classical rewarding scheme of Pong we show how competitive and collaborative behaviors emerge. We also describe the progression from competitive to collaborative behavior when the incentive to cooperate is increased. Finally we show how learning by playing against another adaptive agent, instead of against a hard-wired algorithm, results in more robust strategies. The present work shows that Deep Q-Networks can become a useful tool for studying decentralized learning of multiagent systems coping with high-dimensional environments.

Multiagent cooperation and competition with deep reinforcement learning

Science.gov (United States)

Kodelja, Dorian; Kuzovkin, Ilya; Korjus, Kristjan; Aru, Juhan; Aru, Jaan; Vicente, Raul

2017-01-01

Evolution of cooperation and competition can appear when multiple adaptive agents share a biological, social, or technological niche. In the present work we study how cooperation and competition emerge between autonomous agents that learn by reinforcement while using only their raw visual input as the state representation. In particular, we extend the Deep Q-Learning framework to multiagent environments to investigate the interaction between two learning agents in the well-known video game Pong. By manipulating the classical rewarding scheme of Pong we show how competitive and collaborative behaviors emerge. We also describe the progression from competitive to collaborative behavior when the incentive to cooperate is increased. Finally we show how learning by playing against another adaptive agent, instead of against a hard-wired algorithm, results in more robust strategies. The present work shows that Deep Q-Networks can become a useful tool for studying decentralized learning of multiagent systems coping with high-dimensional environments. PMID:28380078

Multiagent cooperation and competition with deep reinforcement learning.

Science.gov (United States)

Tampuu, Ardi; Matiisen, Tambet; Kodelja, Dorian; Kuzovkin, Ilya; Korjus, Kristjan; Aru, Juhan; Aru, Jaan; Vicente, Raul

2017-01-01

Evolution of cooperation and competition can appear when multiple adaptive agents share a biological, social, or technological niche. In the present work we study how cooperation and competition emerge between autonomous agents that learn by reinforcement while using only their raw visual input as the state representation. In particular, we extend the Deep Q-Learning framework to multiagent environments to investigate the interaction between two learning agents in the well-known video game Pong. By manipulating the classical rewarding scheme of Pong we show how competitive and collaborative behaviors emerge. We also describe the progression from competitive to collaborative behavior when the incentive to cooperate is increased. Finally we show how learning by playing against another adaptive agent, instead of against a hard-wired algorithm, results in more robust strategies. The present work shows that Deep Q-Networks can become a useful tool for studying decentralized learning of multiagent systems coping with high-dimensional environments.

What good are actions? Accelerating learning using learned action priors

CSIR Research Space (South Africa)

Rosman, Benjamin S

2012-11-01

Full Text Available The computational complexity of learning in sequential decision problems grows exponentially with the number of actions available to the agent at each state. We present a method for accelerating this process by learning action priors that express...

CLEANing the Reward: Counterfactual Actions to Remove Exploratory Action Noise in Multiagent Learning

Science.gov (United States)

HolmesParker, Chris; Taylor, Mathew E.; Tumer, Kagan; Agogino, Adrian

2014-01-01

Learning in multiagent systems can be slow because agents must learn both how to behave in a complex environment and how to account for the actions of other agents. The inability of an agent to distinguish between the true environmental dynamics and those caused by the stochastic exploratory actions of other agents creates noise in each agent's reward signal. This learning noise can have unforeseen and often undesirable effects on the resultant system performance. We define such noise as exploratory action noise, demonstrate the critical impact it can have on the learning process in multiagent settings, and introduce a reward structure to effectively remove such noise from each agent's reward signal. In particular, we introduce Coordinated Learning without Exploratory Action Noise (CLEAN) rewards and empirically demonstrate their benefits

Agent Supported Serious Game Environment

Science.gov (United States)

Terzidou, Theodouli; Tsiatsos, Thrasyvoulos; Miliou, Christina; Sourvinou, Athanasia

2016-01-01

This study proposes and applies a novel concept for an AI enhanced serious game collaborative environment as a supplementary learning tool in tertiary education. It is based on previous research that investigated pedagogical agents for a serious game in the OpenSim environment. The proposed AI features to support the serious game are the…

Adversarial Reinforcement Learning in a Cyber Security Simulation}

OpenAIRE

Elderman, Richard; Pater, Leon; Thie, Albert; Drugan, Madalina; Wiering, Marco

2017-01-01

This paper focuses on cyber-security simulations in networks modeled as a Markov game with incomplete information and stochastic elements. The resulting game is an adversarial sequential decision making problem played with two agents, the attacker and defender. The two agents pit one reinforcement learning technique, like neural networks, Monte Carlo learning and Q-learning, against each other and examine their effectiveness against learning opponents. The results showed that Monte Carlo lear...

A Distributed Intelligent E-Learning System

Science.gov (United States)

Kristensen, Terje

2016-01-01

An E-learning system based on a multi-agent (MAS) architecture combined with the Dynamic Content Manager (DCM) model of E-learning, is presented. We discuss the benefits of using such a multi-agent architecture. Finally, the MAS architecture is compared with a pure service-oriented architecture (SOA). This MAS architecture may also be used within…

Learning Strategic Sophistication

NARCIS (Netherlands)

Blume, A.; DeJong, D.V.; Maier, M.

2005-01-01

We experimentally investigate coordination games in which cognition plays an important role, i.e. where outcomes are affected by the agents level of understanding of the game and the beliefs they form about each others understanding.We ask whether and when repeated exposure permits agents to learn

An Improved Reinforcement Learning System Using Affective Factors

Directory of Open Access Journals (Sweden)

Takashi Kuremoto

2013-07-01

Full Text Available As a powerful and intelligent machine learning method, reinforcement learning (RL has been widely used in many fields such as game theory, adaptive control, multi-agent system, nonlinear forecasting, and so on. The main contribution of this technique is its exploration and exploitation approaches to find the optimal solution or semi-optimal solution of goal-directed problems. However, when RL is applied to multi-agent systems (MASs, problems such as “curse of dimension”, “perceptual aliasing problem”, and uncertainty of the environment constitute high hurdles to RL. Meanwhile, although RL is inspired by behavioral psychology and reward/punishment from the environment is used, higher mental factors such as affects, emotions, and motivations are rarely adopted in the learning procedure of RL. In this paper, to challenge agents learning in MASs, we propose a computational motivation function, which adopts two principle affective factors “Arousal” and “Pleasure” of Russell’s circumplex model of affects, to improve the learning performance of a conventional RL algorithm named Q-learning (QL. Compared with the conventional QL, computer simulations of pursuit problems with static and dynamic preys were carried out, and the results showed that the proposed method results in agents having a faster and more stable learning performance.

Transformación del Q-Learning para el Aprendizaje en Agentes JADE

Directory of Open Access Journals (Sweden)

Nayma Cepero-Pérez

2015-06-01

Full Text Available El aumento de la interacción entre los sistemas informáticos ha modificado la forma tradicional de analizarlos y desarrollarlos. La necesidad de la interacción entre los componentes del sistema es cada vez más importante para poder resolver tareas conjuntas, que de forma individual serían muy costosas o incluso imposibles de desarrollar. Los sistemas multi-agente ofrecen una arquitectura interesante y completa para ejecutar tareas distribuidas que cooperan entre sí. La creación de un sistema multi-agente o un agente requiere de gran esfuerzo por lo que se han adoptado métodos como los patrones de implementación. El patrón Proactive Obsever_JADE permite crear los agentes e incluirle en cada uno comportamientos dotados de inteligencia que pueden evolucionar utilizando técnicas de aprendizaje automático. El aprendizaje por refuerzo es una técnica del aprendizaje automático que permite a los agentes aprender a través de interacciones de prueba y error, en un ambiente dinámico. El aprendizaje por refuerzo en sistemas multi-agente ofrece nuevos retos derivados de la distribución del aprendizaje, como pueden ser la necesidad de la coordinación entre agentes o la distribución del conocimiento, que deben ser analizados y tratados.

A self-taught artificial agent for multi-physics computational model personalization.

Science.gov (United States)

Neumann, Dominik; Mansi, Tommaso; Itu, Lucian; Georgescu, Bogdan; Kayvanpour, Elham; Sedaghat-Hamedani, Farbod; Amr, Ali; Haas, Jan; Katus, Hugo; Meder, Benjamin; Steidl, Stefan; Hornegger, Joachim; Comaniciu, Dorin

2016-12-01

Personalization is the process of fitting a model to patient data, a critical step towards application of multi-physics computational models in clinical practice. Designing robust personalization algorithms is often a tedious, time-consuming, model- and data-specific process. We propose to use artificial intelligence concepts to learn this task, inspired by how human experts manually perform it. The problem is reformulated in terms of reinforcement learning. In an off-line phase, Vito, our self-taught artificial agent, learns a representative decision process model through exploration of the computational model: it learns how the model behaves under change of parameters. The agent then automatically learns an optimal strategy for on-line personalization. The algorithm is model-independent; applying it to a new model requires only adjusting few hyper-parameters of the agent and defining the observations to match. The full knowledge of the model itself is not required. Vito was tested in a synthetic scenario, showing that it could learn how to optimize cost functions generically. Then Vito was applied to the inverse problem of cardiac electrophysiology and the personalization of a whole-body circulation model. The obtained results suggested that Vito could achieve equivalent, if not better goodness of fit than standard methods, while being more robust (up to 11% higher success rates) and with faster (up to seven times) convergence rate. Our artificial intelligence approach could thus make personalization algorithms generalizable and self-adaptable to any patient and any model. Copyright © 2016. Published by Elsevier B.V.

A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents

DEFF Research Database (Denmark)

Goldschmidt, Dennis; Manoonpong, Poramate; Dasgupta, Sakyasingha

2017-01-01

in artificial agents. The model consists of a path integration mechanism, reward-modulated global learning, random search, and action selection. The path integration mechanism integrates compass and odometric cues to compute a vectorial representation of the agent's current location as neural activity patterns...

The Dynamics of Learning and the Emergence of Distributed Adaption

National Research Council Canada - National Science Library

Crutchfield, James P

2006-01-01

.... The second goal was to adapt this single-agent learning theory and associated learning algorithms to the distributed setting in which a population of autonomous agents communicate to achieve a desired group task...

[Connectionist models of social learning: a case of learning by observing a simple task].

Science.gov (United States)

Paignon, A; Desrichard, O; Bollon, T

2004-03-01

This article proposes a connectionist model of the social learning theory developed by Bandura (1977). The theory posits that an individual in an interactive situation is capable of learning new behaviours merely by observing them in others. Such learning is acquired through an initial phase in which the individual memorizes what he has observed (observation phase), followed by a second phase where he puts the recorded observations to use as a guide for adjusting his own behaviour (reproduction phase). We shall refer to the two above-mentioned phases to demonstrate that it is conceivable to simulate learning by observation otherwise than through the recording of perceived information using symbolic representation. To this end we shall rely on the formalism of ecological neuron networks (Parisi, Cecconi, & Nolfi, 1990) to implement an agent provided with the major processes identified as essential to learning through observation. The connectionist model so designed shall implement an agent capable of recording perceptive information and producing motor behaviours. The learning situation we selected associates an agent demonstrating goal-achievement behaviour and an observer agent learning the same behaviour by observation. Throughout the acquisition phase, the demonstrator supervises the observer's learning process based on association between spatial information (input) and behavioural information (output). Representation thus constructed then serves as an adjustment guide during the production phase, involving production by the observer of a sequence of actions which he compares to the representation stored in distributed form as constructed through observation. An initial simulation validates model architecture by confirming the requirement for both phases identified in the literature (Bandura, 1977) to simulate learning through observation. The representation constructed over the observation phase evidences acquisition of observed behaviours, although this phase

Behavioral effects of nerve agents: laboratory animal models

International Nuclear Information System (INIS)

Myers, T. M.

2009-01-01

Diverse and often subtle behavioral consequences have been reported for humans exposed to nerve agents. Laboratory studies of nerve agent exposure offer rigorous control over important variables, but species other than man must be used. Nonhuman primate models offer the best means of identifying the toxic nervous system effects of nerve agent insult and the countermeasures best capable of preventing or attenuating these effects. Comprehensive behavioral models must evaluate preservation and recovery of function as well as new learning ability. The throughput and sensitivity of the tests chosen are important considerations. A few nonhuman primate studies will be discussed to elaborate recent successes, current limitations, and future directions.(author)

A Social-Cognitive Framework for Pedagogical Agents as Learning Companions

Science.gov (United States)

Kim, Yanghee; Baylor, Amy L.

2006-01-01

Teaching and learning are highly social activities. Seminal psychologists such as Vygotsky, Piaget, and Bandura have theorized that social interaction is a key mechanism in the process of learning and development. In particular, the benefits of peer interaction for learning and motivation in classrooms have been broadly demonstrated through…

Belief reward shaping in reinforcement learning

CSIR Research Space (South Africa)

Marom, O

2018-02-01

Full Text Available A key challenge in many reinforcement learning problems is delayed rewards, which can significantly slow down learning. Although reward shaping has previously been introduced to accelerate learning by bootstrapping an agent with additional...

Behavior Self-Organization in Multi-Agent Learning

National Research Council Canada - National Science Library

Bay, John

1999-01-01

There are four primary results of the first year of the project: It was discovered that clustering algorithms for pre-sorting high-dimensional datasets was not effective in improving subsequent processing by reinforcement learning methods...

Who Knows? Metacognitive Social Learning Strategies.

Science.gov (United States)

Heyes, Cecilia

2016-03-01

To make good use of learning from others (social learning), we need to learn from the right others; from agents who know better than we do. Research on social learning strategies (SLSs) has identified rules that focus social learning on the right agents, and has shown that the behaviour of many animals conforms to these rules. However, it has not asked what the rules are made of, that is, about the cognitive processes implementing SLSs. Here, I suggest that most SLSs depend on domain-general, sensorimotor processes. However, some SLSs have the characteristics tacitly ascribed to all of them. These metacognitive SLSs represent 'who knows' in a conscious, reportable way, and have the power to promote cultural evolution. Copyright © 2015 Elsevier Ltd. All rights reserved.

Preparing culture change agents for academic medicine in a multi-institutional consortium: the C - change learning action network.

Science.gov (United States)

Pololi, Linda H; Krupat, Edward; Schnell, Eugene R; Kern, David E

2013-01-01

Research suggests an ongoing need for change in the culture of academic medicine. This article describes the structure, activities and evaluation of a culture change project: the C - Change Learning Action Network (LAN) and its impact on participants. The LAN was developed to create the experience of a culture that would prepare participants to facilitate a culture in academic medicine that would be more collaborative, inclusive, relational, and that supports the humanity and vitality of faculty. Purposefully diverse faculty, leaders, and deans from 5 US medical schools convened in 2 1/2-day meetings biannually over 4 years. LAN meetings employed experiential, cognitive, and affective learning modes; innovative dialogue strategies; and reflective practice aimed at facilitating deep dialogue, relationship formation, collaboration, authenticity, and transformative learning to help members experience the desired culture. Robust aggregated qualitative and quantitative data collected from the 5 schools were used to inform and stimulate culture-change plans. Quantitative and qualitative evaluation methods were used. Participants indicated that a safe, supportive, inclusive, collaborative culture was established in LAN and highly valued. LAN members reported a deepened understanding of organizational change, new and valued interpersonal connections, increased motivation and resilience, new skills and approaches, increased self-awareness and personal growth, emotional connection to the issues of diversity and inclusion, and application of new learnings in their work. A carefully designed multi-institutional learning community can transform the way participants experience and view institutional culture. It can motivate and prepare them to be change agents in their own institutions. Copyright © 2013 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council on CME, Association for Hospital Medical

An Analysis Of Personalized Learning Systems For Navy Training And Education Settings

Science.gov (United States)

2016-12-01

student engagement and learning outcomes in higher education. The survey showed that, although 70 percent of students do prefer a learning environment...believable pedagogical agents. These intelligent agents can enhance student engagement with learning material by creating rapport, inciting enthusiasm

Rethinking Culture and Education

Science.gov (United States)

Stambach, Amy

2012-01-01

The author reviews three books that provide complementary and thought-provoking insights. The three books under review are: (1) "Reproducing class: education, neoliberalism, and the rise of the new middle class in Istanbul," by Henry J. Rutz and Erol M. Balkan; (2) "Technology, culture, family: influences on home life," by…

Bridging humans via agent networks

International Nuclear Information System (INIS)

Ishida, Toru

1994-01-01

Recent drastic advance in telecommunication networks enabled the human organization of new class, teleorganization, which differ from any existing organization in that the organization which is easy to create by using telecommunication networks is virtual and remote, that people can join multiple organizations simultaneously, and that the organization can involve people who may not know each other. In order to enjoy the recent advance in telecommunication, the agent networks to help people organize themselves are needed. In this paper, an architecture of agent networks, in which each agent learns the preference or the utility functioin of the owner, and acts on behalf of the owner in maintaining the organization, is proposed. When an agent networks supports a human organization, the conventional human interface is divided into personal and social interfaces. The functionalities of the social interface in teleconferencing and telelearning were investigated. In both cases, the existence of B-ISDN is assumed, and the extension to the business meeting scheduling using personal handy phone (PHS) networks with personal digital assistant (PDA) terminals is expected. These circumstances are described. Mutual selection protocols (MSP) and their dynamic properties are explained. (K.I.)

Visual Stereotypes and Virtual Pedagogical Agents

Science.gov (United States)

Haake, Magnus; Gulz, Agneta

2008-01-01

The paper deals with the use of visual stereotypes in virtual pedagogical agents and its potential impact in digital learning environments. An analysis of the concept of visual stereotypes is followed by a discussion of affordances and drawbacks as to their use in the context of traditional media. Next, the paper explores whether virtual…

Pragmatically Framed Cross-Situational Noun Learning Using Computational Reinforcement Models.

Science.gov (United States)

Najnin, Shamima; Banerjee, Bonny

2018-01-01

Cross-situational learning and social pragmatic theories are prominent mechanisms for learning word meanings (i.e., word-object pairs). In this paper, the role of reinforcement is investigated for early word-learning by an artificial agent. When exposed to a group of speakers, the agent comes to understand an initial set of vocabulary items belonging to the language used by the group. Both cross-situational learning and social pragmatic theory are taken into account. As social cues, joint attention and prosodic cues in caregiver's speech are considered. During agent-caregiver interaction, the agent selects a word from the caregiver's utterance and learns the relations between that word and the objects in its visual environment. The "novel words to novel objects" language-specific constraint is assumed for computing rewards. The models are learned by maximizing the expected reward using reinforcement learning algorithms [i.e., table-based algorithms: Q-learning, SARSA, SARSA-λ, and neural network-based algorithms: Q-learning for neural network (Q-NN), neural-fitted Q-network (NFQ), and deep Q-network (DQN)]. Neural network-based reinforcement learning models are chosen over table-based models for better generalization and quicker convergence. Simulations are carried out using mother-infant interaction CHILDES dataset for learning word-object pairings. Reinforcement is modeled in two cross-situational learning cases: (1) with joint attention (Attentional models), and (2) with joint attention and prosodic cues (Attentional-prosodic models). Attentional-prosodic models manifest superior performance to Attentional ones for the task of word-learning. The Attentional-prosodic DQN outperforms existing word-learning models for the same task.

EPA Science Matters Newsletter: Chemical Warfare Agent Analytical Standards Facilitate Lab Testing (Published November 2013)

Science.gov (United States)

Learn about the EPA chemists' efforts to develop methods for detecting extremely low concentrations of nerve agents, such as sarin, VX, soman and cyclohexyl sarin, and the blister agent sulfur mustard.

The Role of Agent Age and Gender for Middle-Grade Girls

Science.gov (United States)

Kim, Yanghee

2016-01-01

Compared to boys, many girls are more aware of a social context in the learning process and perform better when the environment supports frequent interactions and social relationships. For these girls, embodied agents (animated on-screen characters acting as tutors) could afford simulated social interactions in computer-based learning and thereby…

Computer-assisted CI fitting: Is the learning capacity of the intelligent agent FOX beneficial for speech understanding?

Science.gov (United States)

Meeuws, Matthias; Pascoal, David; Bermejo, Iñigo; Artaso, Miguel; De Ceulaer, Geert; Govaerts, Paul J

2017-07-01

The software application FOX ('Fitting to Outcome eXpert') is an intelligent agent to assist in the programing of cochlear implant (CI) processors. The current version utilizes a mixture of deterministic and probabilistic logic which is able to improve over time through a learning effect. This study aimed at assessing whether this learning capacity yields measurable improvements in speech understanding. A retrospective study was performed on 25 consecutive CI recipients with a median CI use experience of 10 years who came for their annual CI follow-up fitting session. All subjects were assessed by means of speech audiometry with open set monosyllables at 40, 55, 70, and 85 dB SPL in quiet with their home MAP. Other psychoacoustic tests were executed depending on the audiologist's clinical judgment. The home MAP and the corresponding test results were entered into FOX. If FOX suggested to make MAP changes, they were implemented and another speech audiometry was performed with the new MAP. FOX suggested MAP changes in 21 subjects (84%). The within-subject comparison showed a significant median improvement of 10, 3, 1, and 7% at 40, 55, 70, and 85 dB SPL, respectively. All but two subjects showed an instantaneous improvement in their mean speech audiometric score. Persons with long-term CI use, who received a FOX-assisted CI fitting at least 6 months ago, display improved speech understanding after MAP modifications, as recommended by the current version of FOX. This can be explained only by intrinsic improvements in FOX's algorithms, as they have resulted from learning. This learning is an inherent feature of artificial intelligence and it may yield measurable benefit in speech understanding even in long-term CI recipients.

Individual Syllabus for Personalized Learner-Centric E-Courses in E-Learning and M-Learning

OpenAIRE

Khaled Nasser ElSayed

2014-01-01

Most of e-learning and m-learning systems are course-centric. These systems provided services that concentrated on course material and pedagogical. They did not take into account varieties of student levels, skills, interests or preferences. This paper provides a design of an approach for personalized and self-adapted agent-based learning systems for enhancing e-learning and mobile learning (m-learning) services to be learner-centric. It presents a modeling of goals of different learners of a...

Dynamical Intention: Integrated Intelligence Modeling for Goal-directed Embodied Agents

Directory of Open Access Journals (Sweden)

Eric Aaron

2016-11-01

Full Text Available Intelligent embodied robots are integrated systems: As they move continuously through their environments, executing behaviors and carrying out tasks, components for low-level and high-level intelligence are integrated in the robot's cognitive system, and cognitive and physical processes combine to create their behavior. For a modeling framework to enable the design and analysis of such integrated intelligence, the underlying representations in the design of the robot should be dynamically sensitive, capable of reflecting both continuous motion and micro-cognitive influences, while also directly representing the necessary beliefs and intentions for goal-directed behavior. In this paper, a dynamical intention-based modeling framework is presented that satisfies these criteria, along with a hybrid dynamical cognitive agent (HDCA framework for employing dynamical intentions in embodied agents. This dynamical intention-HDCA (DI-HDCA modeling framework is a fusion of concepts from spreading activation networks, hybrid dynamical system models, and the BDI (belief-desire-intention theory of goal-directed reasoning, adapted and employed unconventionally to meet entailments of environment and embodiment. The paper presents two kinds of autonomous agent learning results that demonstrate dynamical intentions and the multi-faceted integration they enable in embodied robots: with a simulated service robot in a grid-world office environment, reactive-level learning minimizes reliance on deliberative-level intelligence, enabling task sequencing and action selection to be distributed over both deliberative and reactive levels; and with a simulated game of Tag, the cognitive-physical integration of an autonomous agent enables the straightforward learning of a user-specified strategy during gameplay, without interruption to the game. In addition, the paper argues that dynamical intentions are consistent with cognitive theory underlying goal-directed behavior, and

Sustainability Learning in Natural Resource Use and Management

Directory of Open Access Journals (Sweden)

J. David Tàbara

2007-12-01

Full Text Available We contribute to the normative discussion on sustainability learning and provide a theoretical integrative framework intended to underlie the main components and interrelations of what learning is required for social learning to become sustainability learning. We demonstrate how this framework has been operationalized in a participatory modeling interface to support processes of natural resource integrated assessment and management. The key modeling components of our view are: structure (S, energy and resources (E, information and knowledge (I, social-ecological change (C, and the size, thresholds, and connections of different social-ecological systems. Our approach attempts to overcome many of the cultural dualisms that exist in the way social and ecological systems are perceived and affect many of the most common definitions of sustainability. Our approach also emphasizes the issue of limits within a total social-ecological system and takes a multiscale, agent-based perspective. Sustainability learning is different from social learning insofar as not all of the outcomes of social learning processes necessarily improve what we consider as essential for the long-term sustainability of social-ecological systems, namely, the co-adaptive systemic capacity of agents to anticipate and deal with the unintended, undesired, and irreversible negative effects of development. Hence, the main difference of sustainability learning from social learning is the content of what is learned and the criteria used to assess such content; these are necessarily related to increasing the capacity of agents to manage, in an integrative and organic way, the total social-ecological system of which they form a part. The concept of sustainability learning and the SEIC social-ecological framework can be useful to assess and communicate the effectiveness of multiple agents to halt or reverse the destructive trends affecting the life-support systems upon which all humans

Empirical agent-based modelling challenges and solutions

CERN Document Server

Barreteau, Olivier

2014-01-01

This instructional book showcases techniques to parameterise human agents in empirical agent-based models (ABM). In doing so, it provides a timely overview of key ABM methodologies and the most innovative approaches through a variety of empirical applications.  It features cutting-edge research from leading academics and practitioners, and will provide a guide for characterising and parameterising human agents in empirical ABM.  In order to facilitate learning, this text shares the valuable experiences of other modellers in particular modelling situations. Very little has been published in the area of empirical ABM, and this contributed volume will appeal to graduate-level students and researchers studying simulation modeling in economics, sociology, ecology, and trans-disciplinary studies, such as topics related to sustainability. In a similar vein to the instruction found in a cookbook, this text provides the empirical modeller with a set of 'recipes'  ready to be implemented. Agent-based modeling (AB...

Reinforcement learning or active inference?

Science.gov (United States)

Friston, Karl J; Daunizeau, Jean; Kiebel, Stefan J

2009-07-29

This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.

Reinforcement learning or active inference?

Directory of Open Access Journals (Sweden)

Karl J Friston

2009-07-01

Full Text Available This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.

Game-theoretic learning and distributed optimization in memoryless multi-agent systems

CERN Document Server

Tatarenko, Tatiana

2017-01-01

This book presents new efficient methods for optimization in realistic large-scale, multi-agent systems. These methods do not require the agents to have the full information about the system, but instead allow them to make their local decisions based only on the local information, possibly obtained during scommunication with their local neighbors. The book, primarily aimed at researchers in optimization and control, considers three different information settings in multi-agent systems: oracle-based, communication-based, and payoff-based. For each of these information types, an efficient optimization algorithm is developed, which leads the system to an optimal state. The optimization problems are set without such restrictive assumptions as convexity of the objective functions, complicated communication topologies, closed-form expressions for costs and utilities, and finiteness of the system’s state space. .

Adaptive vs. eductive learning : Theory and evidence

NARCIS (Netherlands)

Bao, T.; Duffy, J.

2014-01-01

Adaptive learning and eductive learning are two widely used ways of modeling learning behavior in macroeconomics. Both approaches yield restrictions on model parameters under which agents are able to learn a rational expectation equilibrium (REE) but these restrictions do not always overlap with one

Incremental Inductive Learning in a Constructivist Agent

Science.gov (United States)

Perotto, Filipo Studzinski; Älvares, Luís Otávio

The constructivist paradigm in Artificial Intelligence has been definitively inaugurated in the earlier 1990's by Drescher's pioneer work [10]. He faces the challenge of design an alternative model for machine learning, founded in the human cognitive developmental process described by Piaget [x]. His effort has inspired many other researchers.

BROA: An agent-based model to recommend relevant Learning Objects from Repository Federations adapted to learner profile

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Paula A. Rodríguez

2013-03-01

Full Text Available Learning Objects (LOs are distinguished from traditional educational resources for their easy and quickly availability through Web-based repositories, from which they are accessed through their metadata. In addition, having a user profile allows an educational recommender system to help the learner to find the most relevant LOs based on their needs and preferences. The aim of this paper is to propose an agent-based model so-called BROA to recommend relevant LOs recovered from Repository Federations as well as LOs adapted to learner profile. The model proposed uses both role and service models of GAIA methodology, and the analysis models of the MAS-CommonKADS methodology. A prototype was built based on this model and validated to obtain some assessing results that are finally presented.

The Reinforcement Learning Competition 2014

OpenAIRE

Dimitrakakis, Christos; Li, Guangliang; Tziortziotis, Nikoalos

2014-01-01

Reinforcement learning is one of the most general problems in artificial intelligence. It has been used to model problems in automated experiment design, control, economics, game playing, scheduling and telecommunications. The aim of the reinforcement learning competition is to encourage the development of very general learning agents for arbitrary reinforcement learning problems and to provide a test-bed for the unbiased evaluation of algorithms.

Deep imitation learning for 3D navigation tasks.

Science.gov (United States)

Hussein, Ahmed; Elyan, Eyad; Gaber, Mohamed Medhat; Jayne, Chrisina

2018-01-01

Deep learning techniques have shown success in learning from raw high-dimensional data in various applications. While deep reinforcement learning is recently gaining popularity as a method to train intelligent agents, utilizing deep learning in imitation learning has been scarcely explored. Imitation learning can be an efficient method to teach intelligent agents by providing a set of demonstrations to learn from. However, generalizing to situations that are not represented in the demonstrations can be challenging, especially in 3D environments. In this paper, we propose a deep imitation learning method to learn navigation tasks from demonstrations in a 3D environment. The supervised policy is refined using active learning in order to generalize to unseen situations. This approach is compared to two popular deep reinforcement learning techniques: deep-Q-networks and Asynchronous actor-critic (A3C). The proposed method as well as the reinforcement learning methods employ deep convolutional neural networks and learn directly from raw visual input. Methods for combining learning from demonstrations and experience are also investigated. This combination aims to join the generalization ability of learning by experience with the efficiency of learning by imitation. The proposed methods are evaluated on 4 navigation tasks in a 3D simulated environment. Navigation tasks are a typical problem that is relevant to many real applications. They pose the challenge of requiring demonstrations of long trajectories to reach the target and only providing delayed rewards (usually terminal) to the agent. The experiments show that the proposed method can successfully learn navigation tasks from raw visual input while learning from experience methods fail to learn an effective policy. Moreover, it is shown that active learning can significantly improve the performance of the initially learned policy using a small number of active samples.

Political learning among youth

DEFF Research Database (Denmark)

Solhaug, Trond; Kristensen, Niels Nørgaard

2014-01-01

This article focuses on students’ first political learning and explores the research question, what dynamic patterns of political learning can be explored among a selection of young, diverse Danish students’ first political interests? The authors use theories of learning in their analytical......, but are active constructors of their political life. Their emotions and social environment are highly important for their political orientation. It is recommended that further research focus on dynamic learning and on arenas for political learning rather than on “single agent studies.” Recommendations...

Gaze Awareness in Agent-Based Early-Childhood Learning Application

OpenAIRE

Akkil , Deepak; Dey , Prasenjit; Salian , Deepshika; Rajput , Nitendra

2017-01-01

Part 6: Interaction with Children; International audience; Use of technological devices for early childhood learning is increasing. Now, kindergarten and primary school children use interactive applications on mobile phones and tablet computers to support and complement classroom learning. With increase in cognitive technologies, there is further potential to make such applications more engaging by understanding the user context. In this paper, we present the Little Bear, a gaze aware pedagog...

Evolutionary and adaptive learning in complex markets: a brief summary

Science.gov (United States)

Hommes, Cars H.

2007-06-01

We briefly review some work on expectations and learning in complex markets, using the familiar demand-supply cobweb model. We discuss and combine two different approaches on learning. According to the adaptive learning approach, agents behave as econometricians using time series observations to form expectations, and update the parameters as more observations become available. This approach has become popular in macro. The second approach has an evolutionary flavor and is sometimes referred to as reinforcement learning. Agents employ different forecasting strategies and evaluate these strategies based upon a fitness measure, e.g. past realized profits. In this framework, boundedly rational agents switch between different, but fixed behavioral rules. This approach has become popular in finance. We combine evolutionary and adaptive learning to model complex markets and discuss whether this theory can match empirical facts and forecasting behavior in laboratory experiments with human subjects.

Activity in the superior temporal sulcus highlights learning competence in an interaction game.

Science.gov (United States)

Haruno, Masahiko; Kawato, Mitsuo

2009-04-08

During behavioral adaptation through interaction with human and nonhuman agents, marked individual differences are seen in both real-life situations and games. However, the underlying neural mechanism is not well understood. We conducted a neuroimaging experiment in which subjects maximized monetary rewards by learning in a prisoner's dilemma game with two computer agents: agent A, a tit-for-tat player who repeats the subject's previous action, and agent B, a simple stochastic cooperator oblivious to the subject's action. Approximately 1/3 of the subjects (group I) learned optimally in relation to both A and B, while another 1/3 (group II) did so only for B. Post-experiment interviews indicated that group I exploited the agent strategies more often than group II. Significant differences in learning-related brain activity between the two groups were only found in the superior temporal sulcus (STS) for both A and B. Furthermore, the learning performance of each group I subject was predictable based on this STS activity, but not in the group II subjects. This differential activity could not be attributed to a behavioral difference since it persisted in relation to agent B for which the two groups behaved similarly. In sharp contrast, the brain structures for reward processing were recruited similarly by both groups. These results suggest that STS provides knowledge of the other agent's strategies for association between action and reward and highlights learning competence during interactive reinforcement learning.

Intelligent agents for e-commerce applications

Science.gov (United States)

Vuppala, Krishna

1999-12-01

This thesis focuses on development of intelligent agent solutions for e-commerce applications. E-Commerce has several complexities like: lack of information about the players, learning the nature of one's business partners/competitors, finding the right business partner to do business with, using the right strategy to get best profit out of the negotiations etc. The agent models developed can be used in any agent solution for e-commerce. Concepts and techniques from Game Theory and Artificial Intelligence are used. The developed models have several advantages over the existing ones as: the models assume the non-availability of information about other players in the market, the models of players get updated over the time as and when new information comes about the players, the negotiation model incorporates the patience levels of the players and expectations from other players in the market. Power industry has been chosen as the application area for the demonstration of the capabilities and usage of the developed agent models. Two e-commerce scenarios where sellers and buyers can go through the power exchanges to bid in auctions, or make bilateral deals outside of the exchange are addressed. In the first scenario agent helps market participants in coordinating strategies with other participants, bidding in auctions by analyzing and understanding the behavior of other participants. In the second scenario, called "Power Traders Assistant" agent helps power trader, who buys and sells power through bilateral negotiations, in negotiating deals with his customers.

Learning Faster by Discovering and Exploiting Object Similarities

Directory of Open Access Journals (Sweden)

Tadej Janež

2013-03-01

Full Text Available In this paper we explore the question: “Is it possible to speed up the learning process of an autonomous agent by performing experiments in a more complex environment (i.e., an environment with a greater number of different objects?” To this end, we use a simple robotic domain, where the robot has to learn a qualitative model predicting the change in the robot's distance to an object. To quantify the environment's complexity, we defined cardinal complexity as the number of objects in the robot's world, and behavioural complexity as the number of objects' distinct behaviours. We propose Error reduction merging (ERM, a new learning method that automatically discovers similarities in the structure of the agent's environment. ERM identifies different types of objects solely from the data measured and merges the observations of objects that behave in the same or similar way in order to speed up the agent's learning. We performed a series of experiments in worlds of increasing complexity. The results in our simple domain indicate that ERM was capable of discovering structural similarities in the data which indeed made the learning faster, clearly superior to conventional learning. This observed trend occurred with various machine learning algorithms used inside the ERM method.

Fuzzy OLAP association rules mining-based modular reinforcement learning approach for multiagent systems.

Science.gov (United States)

Kaya, Mehmet; Alhajj, Reda

2005-04-01

Multiagent systems and data mining have recently attracted considerable attention in the field of computing. Reinforcement learning is the most commonly used learning process for multiagent systems. However, it still has some drawbacks, including modeling other learning agents present in the domain as part of the state of the environment, and some states are experienced much less than others, or some state-action pairs are never visited during the learning phase. Further, before completing the learning process, an agent cannot exhibit a certain behavior in some states that may be experienced sufficiently. In this study, we propose a novel multiagent learning approach to handle these problems. Our approach is based on utilizing the mining process for modular cooperative learning systems. It incorporates fuzziness and online analytical processing (OLAP) based mining to effectively process the information reported by agents. First, we describe a fuzzy data cube OLAP architecture which facilitates effective storage and processing of the state information reported by agents. This way, the action of the other agent, not even in the visual environment. of the agent under consideration, can simply be predicted by extracting online association rules, a well-known data mining technique, from the constructed data cube. Second, we present a new action selection model, which is also based on association rules mining. Finally, we generalize not sufficiently experienced states, by mining multilevel association rules from the proposed fuzzy data cube. Experimental results obtained on two different versions of a well-known pursuit domain show the robustness and effectiveness of the proposed fuzzy OLAP mining based modular learning approach. Finally, we tested the scalability of the approach presented in this paper and compared it with our previous work on modular-fuzzy Q-learning and ordinary Q-learning.

Drug: D06702 [KEGG MEDICUS

Lifescience Database Archive (English)

Full Text Available D06702 Crude ... Drug Processed ginger (JP17) [6]-Shogaol [CPD:C10494], [6]-Gingerol [CPD:C10462], (8)-Ging...erol [CPD:C17495], (10)-Gingerol [CPD:C17496], Zingerone [CPD:C17497], Zingiberene ... 5100 ... Zingiberaceae (ginger family) Zingiber officinale rhizome Major component: Gingerol [CPD:C10462 C17495 C17496] ... PubChem: 47208353 ...

Building v/s Exploring Models: Comparing Learning of Evolutionary Processes through Agent-based Modeling

Science.gov (United States)

Wagh, Aditi

Two strands of work motivate the three studies in this dissertation. Evolutionary change can be viewed as a computational complex system in which a small set of rules operating at the individual level result in different population level outcomes under different conditions. Extensive research has documented students' difficulties with learning about evolutionary change (Rosengren et al., 2012), particularly in terms of levels slippage (Wilensky & Resnick, 1999). Second, though building and using computational models is becoming increasingly common in K-12 science education, we know little about how these two modalities compare. This dissertation adopts agent-based modeling as a representational system to compare these modalities in the conceptual context of micro-evolutionary processes. Drawing on interviews, Study 1 examines middle-school students' productive ways of reasoning about micro-evolutionary processes to find that the specific framing of traits plays a key role in whether slippage explanations are cued. Study 2, which was conducted in 2 schools with about 150 students, forms the crux of the dissertation. It compares learning processes and outcomes when students build their own models or explore a pre-built model. Analysis of Camtasia videos of student pairs reveals that builders' and explorers' ways of accessing rules, and sense-making of observed trends are of a different character. Builders notice rules through available blocks-based primitives, often bypassing their enactment while explorers attend to rules primarily through the enactment. Moreover, builders' sense-making of observed trends is more rule-driven while explorers' is more enactment-driven. Pre and posttests reveal that builders manifest a greater facility with accessing rules, providing explanations manifesting targeted assembly. Explorers use rules to construct explanations manifesting non-targeted assembly. Interviews reveal varying degrees of shifts away from slippage in both

Learning to make collective decisions: the impact of confidence escalation.

Science.gov (United States)

Mahmoodi, Ali; Bang, Dan; Ahmadabadi, Majid Nili; Bahrami, Bahador

2013-01-01

Little is known about how people learn to take into account others' opinions in joint decisions. To address this question, we combined computational and empirical approaches. Human dyads made individual and joint visual perceptual decision and rated their confidence in those decisions (data previously published). We trained a reinforcement (temporal difference) learning agent to get the participants' confidence level and learn to arrive at a dyadic decision by finding the policy that either maximized the accuracy of the model decisions or maximally conformed to the empirical dyadic decisions. When confidences were shared visually without verbal interaction, RL agents successfully captured social learning. When participants exchanged confidences visually and interacted verbally, no collective benefit was achieved and the model failed to predict the dyadic behaviour. Behaviourally, dyad members' confidence increased progressively and verbal interaction accelerated this escalation. The success of the model in drawing collective benefit from dyad members was inversely related to confidence escalation rate. The findings show an automated learning agent can, in principle, combine individual opinions and achieve collective benefit but the same agent cannot discount the escalation suggesting that one cognitive component of collective decision making in human may involve discounting of overconfidence arising from interactions.

Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving

OpenAIRE

Shalev-Shwartz, Shai; Shammah, Shaked; Shashua, Amnon

2016-01-01

Autonomous driving is a multi-agent setting where the host vehicle must apply sophisticated negotiation skills with other road users when overtaking, giving way, merging, taking left and right turns and while pushing ahead in unstructured urban roadways. Since there are many possible scenarios, manually tackling all possible cases will likely yield a too simplistic policy. Moreover, one must balance between unexpected behavior of other drivers/pedestrians and at the same time not to be too de...

The need for and development of behaviourally realistic agents

NARCIS (Netherlands)

Jager, W; Janssen, M; Sichman, JS; Bousquet, F; Davidsson, P

2003-01-01

In this paper we argue that simulating complex systems involving human behaviour requires agent rules based on a theoretically rooted structure that captures basic behavioural processes. Essential components of such a structure involve needs, decision-making processes and learning. Such a structure

Multi-agent models of spatial cognition, learning and complex choice behavior in urban environments

NARCIS (Netherlands)

Arentze, Theo; Timmermans, Harry; Portugali, J.

2006-01-01

This chapter provides an overview of ongoing research projects in the DDSS research program at TUE related to multi-agents. Projects include (a) the use of multi-agent models and concepts of artificial intelligence to develop models of activity-travel behavior; (b) the use of a multi-agent model to

Enhanced risk management by an emerging multi-agent architecture

Science.gov (United States)

Lin, Sin-Jin; Hsu, Ming-Fu

2014-07-01

Classification in imbalanced datasets has attracted much attention from researchers in the field of machine learning. Most existing techniques tend not to perform well on minority class instances when the dataset is highly skewed because they focus on minimising the forecasting error without considering the relative distribution of each class. This investigation proposes an emerging multi-agent architecture, grounded on cooperative learning, to solve the class-imbalanced classification problem. Additionally, this study deals further with the obscure nature of the multi-agent architecture and expresses comprehensive rules for auditors. The results from this study indicate that the presented model performs satisfactorily in risk management and is able to tackle a highly class-imbalanced dataset comparatively well. Furthermore, the knowledge visualised process, supported by real examples, can assist both internal and external auditors who must allocate limited detecting resources; they can take the rules as roadmaps to modify the auditing programme.

Emergent Learning and Learning Ecologies in Web 2.0

Directory of Open Access Journals (Sweden)

Roy Williams

2011-03-01

Full Text Available This paper describes emergent learning and situates it within learning networks and systems and the broader learning ecology of Web 2.0. It describes the nature of emergence and emergent learning and the conditions that enable emergent, self-organised learning to occur and to flourish. Specifically, it explores whether emergent learning can be validated and self-correcting and whether it is possible to link or integrate emergent and prescribed learning. It draws on complexity theory, communities of practice, and the notion of connectivism to develop some of the foundations for an analytic framework, for enabling and managing emergent learning and networks in which agents and systems co-evolve. It then examines specific cases of learning to test and further develop the analytic framework.The paper argues that although social networking media increase the potential range and scope for emergent learning exponentially, considerable effort is required to ensure an effective balance between openness and constraint. It is possible to manage the relationship between prescriptive and emergent learning, both of which need to be part of an integrated learning ecology.

Mobile Agent-Based Software Systems Modeling Approaches: A Comparative Study

Directory of Open Access Journals (Sweden)

Aissam Belghiat

2016-06-01

Full Text Available Mobile agent-based applications are special type of software systems which take the advantages of mobile agents in order to provide a new beneficial paradigm to solve multiple complex problems in several fields and areas such as network management, e-commerce, e-learning, etc. Likewise, we notice lack of real applications based on this paradigm and lack of serious evaluations of their modeling approaches. Hence, this paper provides a comparative study of modeling approaches of mobile agent-based software systems. The objective is to give the reader an overview and a thorough understanding of the work that has been done and where the gaps in the research are.

Imitative learning as a connector of collective brains.

Directory of Open Access Journals (Sweden)

José F Fontanari

Full Text Available The notion that cooperation can aid a group of agents to solve problems more efficiently than if those agents worked in isolation is prevalent in computer science and business circles. Here we consider a primordial form of cooperation - imitative learning - that allows an effective exchange of information between agents, which are viewed as the processing units of a social intelligence system or collective brain. In particular, we use agent-based simulations to study the performance of a group of agents in solving a cryptarithmetic problem. An agent can either perform local random moves to explore the solution space of the problem or imitate a model agent - the best performing agent in its influence network. There is a trade-off between the number of agents N and the imitation probability p, and for the optimal balance between these parameters we observe a thirtyfold diminution in the computational cost to find the solution of the cryptarithmetic problem as compared with the independent search. If those parameters are chosen far from the optimal setting, however, then imitative learning can impair greatly the performance of the group.

Manufacturing Scheduling Using Colored Petri Nets and Reinforcement Learning

Directory of Open Access Journals (Sweden)

Maria Drakaki

2017-02-01

Full Text Available Agent-based intelligent manufacturing control systems are capable to efficiently respond and adapt to environmental changes. Manufacturing system adaptation and evolution can be addressed with learning mechanisms that increase the intelligence of agents. In this paper a manufacturing scheduling method is presented based on Timed Colored Petri Nets (CTPNs and reinforcement learning (RL. CTPNs model the manufacturing system and implement the scheduling. In the search for an optimal solution a scheduling agent uses RL and in particular the Q-learning algorithm. A warehouse order-picking scheduling is presented as a case study to illustrate the method. The proposed scheduling method is compared to existing methods. Simulation and state space results are used to evaluate performance and identify system properties.

Multidimensional Learner Model In Intelligent Learning System

Science.gov (United States)

Deliyska, B.; Rozeva, A.

2009-11-01

The learner model in an intelligent learning system (ILS) has to ensure the personalization (individualization) and the adaptability of e-learning in an online learner-centered environment. ILS is a distributed e-learning system whose modules can be independent and located in different nodes (servers) on the Web. This kind of e-learning is achieved through the resources of the Semantic Web and is designed and developed around a course, group of courses or specialty. An essential part of ILS is learner model database which contains structured data about learner profile and temporal status in the learning process of one or more courses. In the paper a learner model position in ILS is considered and a relational database is designed from learner's domain ontology. Multidimensional modeling agent for the source database is designed and resultant learner data cube is presented. Agent's modules are proposed with corresponding algorithms and procedures. Multidimensional (OLAP) analysis guidelines on the resultant learner module for designing dynamic learning strategy have been highlighted.

Bottom-up learning of hierarchical models in a class of deterministic POMDP environments

Directory of Open Access Journals (Sweden)

Itoh Hideaki

2015-09-01

Full Text Available The theory of partially observable Markov decision processes (POMDPs is a useful tool for developing various intelligent agents, and learning hierarchical POMDP models is one of the key approaches for building such agents when the environments of the agents are unknown and large. To learn hierarchical models, bottom-up learning methods in which learning takes place in a layer-by-layer manner from the lowest to the highest layer are already extensively used in some research fields such as hidden Markov models and neural networks. However, little attention has been paid to bottom-up approaches for learning POMDP models. In this paper, we present a novel bottom-up learning algorithm for hierarchical POMDP models and prove that, by using this algorithm, a perfect model (i.e., a model that can perfectly predict future observations can be learned at least in a class of deterministic POMDP environments

Multi-agent system for Knowledge-based recommendation of Learning Objects

Directory of Open Access Journals (Sweden)

Paula Andrea RODRÍGUEZ MARÍN

2015-12-01

Full Text Available Learning Object (LO is a content unit being used within virtual learning environments, which -once found and retrieved- may assist students in the teaching - learning process. Such LO search and retrieval are recently supported and enhanced by data mining techniques. In this sense, clustering can be used to find groups holding similar LOs so that from obtained groups, knowledge-based recommender systems (KRS can recommend more adapted and relevant LOs. In particular, prior knowledge come from LOs previously selected, liked and ranked by the student to whom the recommendation will be performed. In this paper, we present a KRS for LOs, which uses a conventional clustering technique, namely K-means, aimed at finding similar LOs and delivering resources adapted to a specific student. Obtained promising results show that proposed KRS is able to both retrieve relevant LO and improve the recommendation precision.Learning Object (LO is a content unit being used within virtual learning environments, which -once found and retrieved- may assist students in the teaching - learning process. Such LO search and retrieval are recently supported and enhanced by data mining techniques. In this sense, clustering can be used to find groups holding similar LOs so that from obtained groups, knowledge-based recommender systems (KRS can recommend more adapted and relevant LOs. In particular, prior knowledge come from LOs previously selected, liked and ranked by the student to whom the recommendation will be performed. In this paper, we present a KRS for LOs, which uses a conventional clustering technique, namely K-means, aimed at finding similar LOs and delivering resources adapted to a specific student. Obtained promising results show that proposed KRS is able to both retrieve relevant LO and improve the recommendation precision.

The Evolution of Social Learning and its Economic Consequences

DEFF Research Database (Denmark)

Bossan, Benjamin; Jann, Ole; Hammerstein, Peter

2015-01-01

to changing environments within one generation by using their respective learning strategy. The frequency of the agent types adapts between generations according to the agents' acquired wealth. During the course of evolution, social learning becomes dominant, resulting in three major effects: First......, for better or worse, the decisions of social learners are more exaggerated than those of individual learners. Second, social learners react with a delay to changes in the environment. Third, the behavior of social learners becomes more and more detached from reality. We argue that our model gives insights......We use an evolutionary model to simulate agents who choose between two options with stochastically varying payoffs. Two types of agents are considered: individual learners, who rely on trial-and-error methods, and social learners, who imitate the wealthiest sampled individual. Agents adapt...

Analysis of the "naming game" with learning errors in communications.

Science.gov (United States)

Lou, Yang; Chen, Guanrong

2015-07-16

Naming game simulates the process of naming an objective by a population of agents organized in a certain communication network. By pair-wise iterative interactions, the population reaches consensus asymptotically. We study naming game with communication errors during pair-wise conversations, with error rates in a uniform probability distribution. First, a model of naming game with learning errors in communications (NGLE) is proposed. Then, a strategy for agents to prevent learning errors is suggested. To that end, three typical topologies of communication networks, namely random-graph, small-world and scale-free networks, are employed to investigate the effects of various learning errors. Simulation results on these models show that 1) learning errors slightly affect the convergence speed but distinctively increase the requirement for memory of each agent during lexicon propagation; 2) the maximum number of different words held by the population increases linearly as the error rate increases; 3) without applying any strategy to eliminate learning errors, there is a threshold of the learning errors which impairs the convergence. The new findings may help to better understand the role of learning errors in naming game as well as in human language development from a network science perspective.

30th International Symposium on Computer and Information Sciences

CERN Document Server

Gelenbe, Erol; Gorbil, Gokce; Lent, Ricardo

2016-01-01

The 30th Anniversary of the ISCIS (International Symposium on Computer and Information Sciences) series of conferences, started by Professor Erol Gelenbe at Bilkent University, Turkey, in 1986, will be held at Imperial College London on September 22-24, 2015. The preceding two ISCIS conferences were held in Krakow, Poland in 2014, and in Paris, France, in 2013.   The Proceedings of ISCIS 2015 published by Springer brings together rigorously reviewed contributions from leading international experts. It explores new areas of research and technological development in computer science, computer engineering, and information technology, and presents new applications in fast changing fields such as information science, computer science and bioinformatics.   The topics covered include (but are not limited to) advances in networking technologies, software defined networks, distributed systems and the cloud, security in the Internet of Things, sensor systems, and machine learning and large data sets.

From particle systems to learning processes. Comment on "Collective learning modeling based on the kinetic theory of active particles" by Diletta Burini, Silvana De Lillo, and Livio Gibelli

Science.gov (United States)

Lachowicz, Mirosław

2016-03-01

The very stimulating paper [6] discusses an approach to perception and learning in a large population of living agents. The approach is based on a generalization of kinetic theory methods in which the interactions between agents are described in terms of game theory. Such an approach was already discussed in Ref. [2-4] (see also references therein) in various contexts. The processes of perception and learning are based on the interactions between agents and therefore the general kinetic theory is a suitable tool for modeling them. However the main question that rises is how the perception and learning processes may be treated in the mathematical modeling. How may we precisely deliver suitable mathematical structures that are able to capture various aspects of perception and learning?

Learning efficient correlated equilibria

KAUST Repository

Borowski, Holly P.

2014-12-15

The majority of distributed learning literature focuses on convergence to Nash equilibria. Correlated equilibria, on the other hand, can often characterize more efficient collective behavior than even the best Nash equilibrium. However, there are no existing distributed learning algorithms that converge to specific correlated equilibria. In this paper, we provide one such algorithm which guarantees that the agents\\' collective joint strategy will constitute an efficient correlated equilibrium with high probability. The key to attaining efficient correlated behavior through distributed learning involves incorporating a common random signal into the learning environment.

Learning efficient correlated equilibria

KAUST Repository

Borowski, Holly P.; Marden, Jason R.; Shamma, Jeff S.

2014-01-01

The majority of distributed learning literature focuses on convergence to Nash equilibria. Correlated equilibria, on the other hand, can often characterize more efficient collective behavior than even the best Nash equilibrium. However, there are no existing distributed learning algorithms that converge to specific correlated equilibria. In this paper, we provide one such algorithm which guarantees that the agents' collective joint strategy will constitute an efficient correlated equilibrium with high probability. The key to attaining efficient correlated behavior through distributed learning involves incorporating a common random signal into the learning environment.

Consensus based on learning game theory with a UAV rendezvous application

Directory of Open Access Journals (Sweden)

Zhongjie Lin

2015-02-01

Full Text Available Multi-agent cooperation problems are becoming more and more attractive in both civilian and military applications. In multi-agent cooperation problems, different network topologies will decide different manners of cooperation between agents. A centralized system will directly control the operation of each agent with information flow from a single centre, while in a distributed system, agents operate separately under certain communication protocols. In this paper, a systematic distributed optimization approach will be established based on a learning game algorithm. The convergence of the algorithm will be proven under the game theory framework. Two typical consensus problems will be analyzed with the proposed algorithm. The contributions of this work are threefold. First, the designed algorithm inherits the properties in learning game theory for problem simplification and proof of convergence. Second, the behaviour of learning endows the algorithm with robustness and autonomy. Third, with the proposed algorithm, the consensus problems will be analyzed from a novel perspective.

Quantum machine learning with glow for episodic tasks and decision games

Science.gov (United States)

Clausen, Jens; Briegel, Hans J.

2018-02-01

We consider a general class of models, where a reinforcement learning (RL) agent learns from cyclic interactions with an external environment via classical signals. Perceptual inputs are encoded as quantum states, which are subsequently transformed by a quantum channel representing the agent's memory, while the outcomes of measurements performed at the channel's output determine the agent's actions. The learning takes place via stepwise modifications of the channel properties. They are described by an update rule that is inspired by the projective simulation (PS) model and equipped with a glow mechanism that allows for a backpropagation of policy changes, analogous to the eligibility traces in RL and edge glow in PS. In this way, the model combines features of PS with the ability for generalization, offered by its physical embodiment as a quantum system. We apply the agent to various setups of an invasion game and a grid world, which serve as elementary model tasks allowing a direct comparison with a basic classical PS agent.

A Learning Object Approach To Evidence based learning

Directory of Open Access Journals (Sweden)

Zabin Visram

2005-06-01

Full Text Available This paper describes the philosophy, development and framework of the body of elements formulated to provide an approach to evidence-based learning sustained by Learning Objects and web based technology Due to the demands for continuous improvement in the delivery of healthcare and in the continuous endeavour to improve the quality of life, there is a continuous need for practitioner's to update their knowledge by accomplishing accredited courses. The rapid advances in medical science has meant increasingly, there is a desperate need to adopt wireless schemes, whereby bespoke courses can be developed to help practitioners keep up with expanding knowledge base. Evidently, without current best evidence, practice risks becoming rapidly out of date, to the detriment of the patient. There is a need to provide a tactical, operational and effective environment, which allows professional to update their education, and complete specialised training, just-in-time, in their own time and location. Following this demand in the marketplace the information engineering group, in combination with several medical and dental schools, set out to develop and design a conceptual framework which form the basis of pioneering research, which at last, enables practitioner's to adopt a philosophy of life long learning. The body and structure of this framework is subsumed under the term Object oriented approach to Evidence Based learning, Just-in-time, via Internet sustained by Reusable Learning Objects (The OEBJIRLO Progression. The technical pillars which permit this concept of life long learning are pivoted by the foundations of object oriented technology, Learning objects, Just-in-time education, Data Mining, intelligent Agent technology, Flash interconnectivity and remote wireless technology, which allow practitioners to update their professional skills, complete specialised training which leads to accredited qualifications. This paper sets out to develop and

A Social-Cognitive Framework for Designing Pedagogical Agents as Learning Companions

OpenAIRE

Kim, Yanghee; Baylor, Amy L.

2006-01-01

Teaching and learning are highly social activities. Seminal psychologists such as Vygotsky, Piaget, and Bandura have theorized that social interaction is a key mechanism in the process of learning and development. In particular, the benefits of peer interaction for learning and motivation in classrooms have been broadly demonstrated through empirical studies. Hence, it would be valuable if computer-based environments could support a mechanism for a peer-interaction. Though no claim of peer eq...

Interaction learning for dynamic movement primitives used in cooperative robotic tasks

DEFF Research Database (Denmark)

Kulvicius, Tomas; Biehl, Martin; Aein, Mohamad Javad

2013-01-01

Abstract Since several years dynamic movement primitives (DMPs) are more and more getting into the center of interest for flexible movement control in robotics. In this study we introduce sensory feedback together with a predictive learning mechanism which allows tightly coupled dual-agent systems...... to learn an adaptive, sensor-driven interaction based on DMPs. The coupled conventional (no-sensors, no learning) DMP-system automatically equilibrates and can still be solved analytically allowing us to derive conditions for stability. When adding adaptive sensor control we can show that both agents learn...

A Multi-Agent Control Architecture for a Robotic Wheelchair

Directory of Open Access Journals (Sweden)

C. Galindo

2006-01-01

Full Text Available Assistant robots like robotic wheelchairs can perform an effective and valuable work in our daily lives. However, they eventually may need external help from humans in the robot environment (particularly, the driver in the case of a wheelchair to accomplish safely and efficiently some tricky tasks for the current technology, i.e. opening a locked door, traversing a crowded area, etc. This article proposes a control architecture for assistant robots designed under a multi-agent perspective that facilitates the participation of humans into the robotic system and improves the overall performance of the robot as well as its dependability. Within our design, agents have their own intentions and beliefs, have different abilities (that include algorithmic behaviours and human skills and also learn autonomously the most convenient method to carry out their actions through reinforcement learning. The proposed architecture is illustrated with a real assistant robot: a robotic wheelchair that provides mobility to impaired or elderly people.

New Trends in Agent-Based Complex Automated Negotiations

CERN Document Server

Zhang, Minjie; Robu, Valentin; Fatima, Shaheen; Matsuo, Tokuro

2012-01-01

Complex Automated Negotiations represent an important, emerging area in the field of Autonomous Agents and Multi-Agent Systems. Automated negotiations can be complex, since there are a lot of factors that characterize such negotiations. These factors include the number of issues, dependencies between these issues,  representation of utilities, the negotiation protocol, the number of parties in the negotiation (bilateral or multi-party), time constraints, etc. Software agents can support automation or simulation of such complex negotiations on the behalf of their owners, and can provide them with efficient bargaining strategies. To realize such a complex automated negotiation, we have to incorporate advanced Artificial Intelligence technologies includes search, CSP, graphical utility models, Bayes nets, auctions, utility graphs, predicting and learning methods. Applications could include e-commerce tools, decision-making support tools, negotiation support tools, collaboration tools, etc. This book aims to pro...

A spatial web/agent-based model to support stakeholders' negotiation regarding land development.

Science.gov (United States)

Pooyandeh, Majeed; Marceau, Danielle J

2013-11-15

Decision making in land management can be greatly enhanced if the perspectives of concerned stakeholders are taken into consideration. This often implies negotiation in order to reach an agreement based on the examination of multiple alternatives. This paper describes a spatial web/agent-based modeling system that was developed to support the negotiation process of stakeholders regarding land development in southern Alberta, Canada. This system integrates a fuzzy analytic hierarchy procedure within an agent-based model in an interactive visualization environment provided through a web interface to facilitate the learning and negotiation of the stakeholders. In the pre-negotiation phase, the stakeholders compare their evaluation criteria using linguistic expressions. Due to the uncertainty and fuzzy nature of such comparisons, a fuzzy Analytic Hierarchy Process is then used to prioritize the criteria. The negotiation starts by a development plan being submitted by a user (stakeholder) through the web interface. An agent called the proposer, which represents the proposer of the plan, receives this plan and starts negotiating with all other agents. The negotiation is conducted in a step-wise manner where the agents change their attitudes by assigning a new set of weights to their criteria. If an agreement is not achieved, a new location for development is proposed by the proposer agent. This process is repeated until a location is found that satisfies all agents to a certain predefined degree. To evaluate the performance of the model, the negotiation was simulated with four agents, one of which being the proposer agent, using two hypothetical development plans. The first plan was selected randomly; the other one was chosen in an area that is of high importance to one of the agents. While the agents managed to achieve an agreement about the location of the land development after three rounds of negotiation in the first scenario, seven rounds were required in the second

A Multi-Agent Based Energy Management Solution for Integrated Buildings and Microgrid System

DEFF Research Database (Denmark)

Anvari-Moghaddam, Amjad; Rahimi-Kian, Ashkan; Mirian, Maryam S.

2017-01-01

-reflex to complex learning agents are designed and implemented to cooperate with each other to reach an optimal operating strategy for the mentioned integrated energy system (IES) while meeting the system’s objectives and related constraints. The optimization process for the EMS is defined as a coordinated......In this paper, an ontology-driven multi-agent based energy management system (EMS) is proposed for monitoring and optimal control of an integrated homes/buildings and microgrid system with various renewable energy resources (RESs) and controllable loads. Different agents ranging from simple...... distributed generation (DG) and demand response (DR) management problem within the studied environment and is solved by the proposed agent-based approach utilizing cooperation and communication among decision agents. To verify the effectiveness and applicability of the proposed multi-agent based EMS, several...

Extended Learning on SOAR

National Research Council Canada - National Science Library

Laird, John E

2006-01-01

The major goal of this project was to develop the science and technology for building autonomous knowledge-rich learning agents - computational systems that have significant competence for performing...

Collaborative E-Learning with Multiple Imaginary Co-Learner: Design, Issues and Implementation

OpenAIRE

Melvin Ballera; Mosbah Mohamed Elssaedi; Ahmed Khalil Zohdy

2013-01-01

Collaborative problem solving in e-learning can take in the form of discussion among learner, creating a highly social learning environment and characterized by participation and interactivity. This paper, designed a collaborative learning environment where agent act as co-learner, can play different roles during interaction. Since different roles have been assigned to the agent, learner will assume that multiple co-learner exists to help and guide him all throughout the ...

Unimodal Learning Enhances Crossmodal Learning in Robotic Audio-Visual Tracking

DEFF Research Database (Denmark)

Shaikh, Danish; Bodenhagen, Leon; Manoonpong, Poramate

2017-01-01

Crossmodal sensory integration is a fundamental feature of the brain that aids in forming an coherent and unified representation of observed events in the world. Spatiotemporally correlated sensory stimuli brought about by rich sensorimotor experiences drive the development of crossmodal integrat...... a non-holonomic robotic agent towards a moving audio-visual target. Simulation results demonstrate that unimodal learning enhances crossmodal learning and improves both the overall accuracy and precision of multisensory orientation response....

Unimodal Learning Enhances Crossmodal Learning in Robotic Audio-Visual Tracking

DEFF Research Database (Denmark)

Shaikh, Danish; Bodenhagen, Leon; Manoonpong, Poramate

2018-01-01

Crossmodal sensory integration is a fundamental feature of the brain that aids in forming an coherent and unified representation of observed events in the world. Spatiotemporally correlated sensory stimuli brought about by rich sensorimotor experiences drive the development of crossmodal integrat...... a non-holonomic robotic agent towards a moving audio-visual target. Simulation results demonstrate that unimodal learning enhances crossmodal learning and improves both the overall accuracy and precision of multisensory orientation response....

Agent Technologies in the Electronic Classroom: Some Pedagogical Issues.

Science.gov (United States)

Dowling, Carolyn

The use of intelligent software agents within computer mediated learning environments has become an important focus of research and development in both AI and educational contexts. Some of the roles envisaged and implemented for these electronic entities involve direct interactions with students, participating in the "social" dimension of the…

Agent-Based Model of Price Competition and Product Differentiation on Congested Networks

OpenAIRE

Lei Zhang; David Levinson; Shanjiang Zhu

2007-01-01

Using consistent agent-based techniques, this research models the decision-making processes of users and infrastructure owner/operators to explore the welfare consequence of price competition, capacity choice, and product differentiation on congested transportation networks. Component models include: (1) An agent-based travel demand model wherein each traveler has learning capabilities and unique characteristics (e.g. value of time); (2) Econometric facility provision cost models; and (3) Rep...

Learning Natural Selection in 4th Grade with Multi-Agent-Based Computational Models

Science.gov (United States)

Dickes, Amanda Catherine; Sengupta, Pratim

2013-01-01

In this paper, we investigate how elementary school students develop multi-level explanations of population dynamics in a simple predator-prey ecosystem, through scaffolded interactions with a multi-agent-based computational model (MABM). The term "agent" in an MABM indicates individual computational objects or actors (e.g., cars), and these…

Agent-Based Personalisation and User Modeling for Personalised Educational Games

NARCIS (Netherlands)

Peeters, M.M.; Bosch, K. van den; Meyer, J.J.C.; Neerincx, M.A

2016-01-01

Personalisation can increase the learning efficacy of educational games by tailoring their content to the needs of the individual learner. This paper presents the Personalised Educational Game Architecture (PEGA). It uses a multi-agent organisation and an ontology to offer learners personalised

An Empirical Study of AI Population Dynamics with Million-agent Reinforcement Learning

OpenAIRE

Yang, Yaodong; Yu, Lantao; Bai, Yiwei; Wang, Jun; Zhang, Weinan; Wen, Ying; Yu, Yong

2017-01-01

In this paper, we conduct an empirical study on discovering the ordered collective dynamics obtained by a population of artificial intelligence (AI) agents. Our intention is to put AI agents into a simulated natural context, and then to understand their induced dynamics at the population level. In particular, we aim to verify if the principles developed in the real world could also be used in understanding an artificially-created intelligent population. To achieve this, we simulate a large-sc...

Conceptualizing Debates in Learning and Educational Research: Toward a Complex Systems Conceptual Framework of Learning

Science.gov (United States)

Jacobson, Michael J.; Kapur, Manu; Reimann, Peter

2016-01-01

This article proposes a conceptual framework of learning based on perspectives and methodologies being employed in the study of complex physical and social systems to inform educational research. We argue that the contexts in which learning occurs are complex systems with elements or agents at different levels--including neuronal, cognitive,…

Coaching: Learning and Using Environment and Agent Models for Advice

Science.gov (United States)

2005-03-31

indicate what section(s) of the thesis use that citation. Mazda Ahmadi, Abolfazl Keighobadi Lamjiri, Mayssam M. Nevisi, Jafar Habibi, and Kambiz...and W.L. Johnson. Animated agents for procedural training in virtual reality: Perception, cognition, and motor control. Applied Artificial Intelligence

Cells, Agents, and Support Vectors in Interaction - Modeling Urban Sprawl based on Machine Learning and Artificial Intelligence Techniques in a Post-Industrial Region

Science.gov (United States)

Rienow, A.; Menz, G.

2015-12-01

Since the beginning of the millennium, artificial intelligence techniques as cellular automata (CA) and multi-agent systems (MAS) have been incorporated into land-system simulations to address the complex challenges of transitions in urban areas as open, dynamic systems. The study presents a hybrid modeling approach for modeling the two antagonistic processes of urban sprawl and urban decline at once. The simulation power of support vector machines (SVM), cellular automata (CA) and multi-agent systems (MAS) are integrated into one modeling framework and applied to the largest agglomeration of Central Europe: the Ruhr. A modified version of SLEUTH (short for Slope, Land-use, Exclusion, Urban, Transport, and Hillshade) functions as the CA component. SLEUTH makes use of historic urban land-use data sets and growth coefficients for the purpose of modeling physical urban expansion. The machine learning algorithm of SVM is applied in order to enhance SLEUTH. Thus, the stochastic variability of the CA is reduced and information about the human and ecological forces driving the local suitability of urban sprawl is incorporated. Subsequently, the supported CA is coupled with the MAS ReHoSh (Residential Mobility and the Housing Market of Shrinking City Systems). The MAS models population patterns, housing prices, and housing demand in shrinking regions based on interactions between household and city agents. Semi-explicit urban weights are introduced as a possibility of modeling from and to the pixel simultaneously. Three scenarios of changing housing preferences reveal the urban development of the region in terms of quantity and location. They reflect the dissemination of sustainable thinking among stakeholders versus the steady dream of owning a house in sub- and exurban areas. Additionally, the outcomes are transferred into a digital petri dish reflecting a synthetic environment with perfect conditions of growth. Hence, the generic growth elements affecting the future

Balancing Two-Player Stochastic Games with Soft Q-Learning

OpenAIRE

Grau-Moya, Jordi; Leibfried, Felix; Bou-Ammar, Haitham

2018-01-01

Within the context of video games the notion of perfectly rational agents can be undesirable as it leads to uninteresting situations, where humans face tough adversarial decision makers. Current frameworks for stochastic games and reinforcement learning prohibit tuneable strategies as they seek optimal performance. In this paper, we enable such tuneable behaviour by generalising soft Q-learning to stochastic games, where more than one agent interact strategically. We contribute both theoretic...

General Video Game AI: Learning from Screen Capture

OpenAIRE

Kunanusont, Kamolwan; Lucas, Simon M.; Perez-Liebana, Diego

2017-01-01

General Video Game Artificial Intelligence is a general game playing framework for Artificial General Intelligence research in the video-games domain. In this paper, we propose for the first time a screen capture learning agent for General Video Game AI framework. A Deep Q-Network algorithm was applied and improved to develop an agent capable of learning to play different games in the framework. After testing this algorithm using various games of different categories and difficulty levels, th...

Lessons from Learning to Have Rational Expectations

OpenAIRE

Lindh, Thomas

1989-01-01

This paper reviews a growing literature investigating how economic agents may learn rational expectations. Fully rational learning requires implausible initial information assumptions, therefore some form of bounded rationality has come into focus. Such learning models often converge to rational expectations equilibria within certain bounds. Convergence analysis has been much simplified by methods from adaptive control theory. Learning stability as a correspondence principle show some promise...

Feature selection for domain knowledge representation through multitask learning

CSIR Research Space (South Africa)

Rosman, Benjamin S

2014-10-01

Full Text Available represent stimuli of interest, and rich feature sets which increase the dimensionality of the space and thus the difficulty of the learning problem. We focus on a multitask reinforcement learning setting, where the agent is learning domain knowledge...

Learning and exploration in action-perception loops.

Science.gov (United States)

Little, Daniel Y; Sommer, Friedrich T

2013-01-01

Discovering the structure underlying observed data is a recurring problem in machine learning with important applications in neuroscience. It is also a primary function of the brain. When data can be actively collected in the context of a closed action-perception loop, behavior becomes a critical determinant of learning efficiency. Psychologists studying exploration and curiosity in humans and animals have long argued that learning itself is a primary motivator of behavior. However, the theoretical basis of learning-driven behavior is not well understood. Previous computational studies of behavior have largely focused on the control problem of maximizing acquisition of rewards and have treated learning the structure of data as a secondary objective. Here, we study exploration in the absence of external reward feedback. Instead, we take the quality of an agent's learned internal model to be the primary objective. In a simple probabilistic framework, we derive a Bayesian estimate for the amount of information about the environment an agent can expect to receive by taking an action, a measure we term the predicted information gain (PIG). We develop exploration strategies that approximately maximize PIG. One strategy based on value-iteration consistently learns faster than previously developed reward-free exploration strategies across a diverse range of environments. Psychologists believe the evolutionary advantage of learning-driven exploration lies in the generalized utility of an accurate internal model. Consistent with this hypothesis, we demonstrate that agents which learn more efficiently during exploration are later better able to accomplish a range of goal-directed tasks. We will conclude by discussing how our work elucidates the explorative behaviors of animals and humans, its relationship to other computational models of behavior, and its potential application to experimental design, such as in closed-loop neurophysiology studies.

Learning and exploration in action-perception loops

Directory of Open Access Journals (Sweden)

Daniel Ying-Jeh Little

2013-03-01

Full Text Available Discovering the structure underlying observed data is a recurring problem in machine learning with important applications in neuroscience. It is also a primary function of the brain. When data can be actively collected in the context of a closed action-perception loop, behavior becomes a critical determinant of learning efficiency. Psychologists studying exploration and curiosity in humans and animals have long argued that learning itself is a primary motivator of behavior. However, the theoretical basis of learning-driven behavior is not well understood. Previous computational studies of behavior have largely focused on the control problem of maximizing acquisition of rewards and have treated learning the structure of data as a secondary objective. Here, we study exploration in the absence of external reward feedback. Instead, we take the quality of an agent's learned internal model to be the primary objective. In a simple probabilistic framework, we derive a Bayesian estimate for the amount of information about the environment an agent can expect to receive by taking an action, a measure we term the predicted information gain (PIG. We develop exploration strategies that approximately maximize PIG. One strategy based on value-iteration consistently learns faster, across a diverse range of environments, than previously developed reward-free exploration strategies. Psychologists believe the evolutionary advantage of learning-driven exploration lies in the generalized utility of an accurate internal model. Consistent with this hypothesis, we demonstrate that agents which learn more efficiently during exploration are later better able to accomplish a range of goal-directed tasks. We will conclude by discussing how our work elucidates the explorative behaviors of animals and humans, its relationship to other computational models of behavior, and its potential application to experimental design, such as in closed-loop neurophysiology studies.

Characterizing Reinforcement Learning Methods through Parameterized Learning Problems

Science.gov (United States)

2011-06-03

extraneous. The agent could potentially adapt these representational aspects by applying methods from feature selection ( Kolter and Ng, 2009; Petrik et al...611–616. AAAI Press. Kolter , J. Z. and Ng, A. Y. (2009). Regularization and feature selection in least-squares temporal difference learning. In A. P

Knowledge models as agents of meaninful learning and knowledge creation.

OpenAIRE

Fermín María González García; Jorge Fernando Veloz Ortiz; Iovanna Alejandra Rodríguez Moreno; Luis Efrén Velos Ortiz; Beatriz Guardián Soto; Antoni Ballester Valori

2013-01-01

The educational change that pushes the current context requires a shift in the unfortunately predominant positivist-behaviourist model that favours mechanical      memoristic learning, ideal breeding ground for the existence and maintenance of conceptual errors, to another cognitive-constructivist that stimulates meaningful learning to allow students to build and master knowledge, therefore to be more creative and critical. We present here a model of knowledge where students construct new...

An adaptive multi-agent memetic system for personalizing e-learning experiences

NARCIS (Netherlands)

Acampora, G.; Gaeta, M.; Munoz, E.; Vitiello, A.

2011-01-01

The rapid changes in modern knowledge, due to exponential growth of information sources, are complicating learners' activity. For this reason, novel approaches are necessary to obtain suitable learning solutions able to generate efficient, personalized and flexible learning experiences. From this

Knowledge-Based Reinforcement Learning for Data Mining

Science.gov (United States)

Kudenko, Daniel; Grzes, Marek

Data Mining is the process of extracting patterns from data. Two general avenues of research in the intersecting areas of agents and data mining can be distinguished. The first approach is concerned with mining an agent’s observation data in order to extract patterns, categorize environment states, and/or make predictions of future states. In this setting, data is normally available as a batch, and the agent’s actions and goals are often independent of the data mining task. The data collection is mainly considered as a side effect of the agent’s activities. Machine learning techniques applied in such situations fall into the class of supervised learning. In contrast, the second scenario occurs where an agent is actively performing the data mining, and is responsible for the data collection itself. For example, a mobile network agent is acquiring and processing data (where the acquisition may incur a certain cost), or a mobile sensor agent is moving in a (perhaps hostile) environment, collecting and processing sensor readings. In these settings, the tasks of the agent and the data mining are highly intertwined and interdependent (or even identical). Supervised learning is not a suitable technique for these cases. Reinforcement Learning (RL) enables an agent to learn from experience (in form of reward and punishment for explorative actions) and adapt to new situations, without a teacher. RL is an ideal learning technique for these data mining scenarios, because it fits the agent paradigm of continuous sensing and acting, and the RL agent is able to learn to make decisions on the sampling of the environment which provides the data. Nevertheless, RL still suffers from scalability problems, which have prevented its successful use in many complex real-world domains. The more complex the tasks, the longer it takes a reinforcement learning algorithm to converge to a good solution. For many real-world tasks, human expert knowledge is available. For example, human

Off-Policy Reinforcement Learning for Synchronization in Multiagent Graphical Games.

Science.gov (United States)

Li, Jinna; Modares, Hamidreza; Chai, Tianyou; Lewis, Frank L; Xie, Lihua

2017-10-01

This paper develops an off-policy reinforcement learning (RL) algorithm to solve optimal synchronization of multiagent systems. This is accomplished by using the framework of graphical games. In contrast to traditional control protocols, which require complete knowledge of agent dynamics, the proposed off-policy RL algorithm is a model-free approach, in that it solves the optimal synchronization problem without knowing any knowledge of the agent dynamics. A prescribed control policy, called behavior policy, is applied to each agent to generate and collect data for learning. An off-policy Bellman equation is derived for each agent to learn the value function for the policy under evaluation, called target policy, and find an improved policy, simultaneously. Actor and critic neural networks along with least-square approach are employed to approximate target control policies and value functions using the data generated by applying prescribed behavior policies. Finally, an off-policy RL algorithm is presented that is implemented in real time and gives the approximate optimal control policy for each agent using only measured data. It is shown that the optimal distributed policies found by the proposed algorithm satisfy the global Nash equilibrium and synchronize all agents to the leader. Simulation results illustrate the effectiveness of the proposed method.

Research and application of multi-agent genetic algorithm in tower defense game

Science.gov (United States)

Jin, Shaohua

2018-04-01

In this paper, a new multi-agent genetic algorithm based on orthogonal experiment is proposed, which is based on multi-agent system, genetic algorithm and orthogonal experimental design. The design of neighborhood competition operator, orthogonal crossover operator, Son and self-learning operator. The new algorithm is applied to mobile tower defense game, according to the characteristics of the game, the establishment of mathematical models, and finally increases the value of the game's monster.

Statistical mechanics of competitive resource allocation using agent-based models

Science.gov (United States)

Chakraborti, Anirban; Challet, Damien; Chatterjee, Arnab; Marsili, Matteo; Zhang, Yi-Cheng; Chakrabarti, Bikas K.

2015-01-01

Demand outstrips available resources in most situations, which gives rise to competition, interaction and learning. In this article, we review a broad spectrum of multi-agent models of competition (El Farol Bar problem, Minority Game, Kolkata Paise Restaurant problem, Stable marriage problem, Parking space problem and others) and the methods used to understand them analytically. We emphasize the power of concepts and tools from statistical mechanics to understand and explain fully collective phenomena such as phase transitions and long memory, and the mapping between agent heterogeneity and physical disorder. As these methods can be applied to any large-scale model of competitive resource allocation made up of heterogeneous adaptive agent with non-linear interaction, they provide a prospective unifying paradigm for many scientific disciplines.

A Courseware to Script Animated Pedagogical Agents in Instructional Material for Elementary Students in English Education

Science.gov (United States)

Hong, Zeng-Wei; Chen, Yen-Lin; Lan, Chien-Ho

2014-01-01

Animated agents are virtual characters who demonstrate facial expressions, gestures, movements, and speech to facilitate students' engagement in the learning environment. Our research developed a courseware that supports a XML-based markup language and an authoring tool for teachers to script animated pedagogical agents in teaching materials. The…

Agent Model Development for Assessing Climate-Induced Geopolitical Instability.

Energy Technology Data Exchange (ETDEWEB)

Boslough, Mark B.; Backus, George A.

2005-12-01

We present the initial stages of development of new agent-based computational methods to generate and test hypotheses about linkages between environmental change and international instability. This report summarizes the first year's effort of an originally proposed three-year Laboratory Directed Research and Development (LDRD) project. The preliminary work focused on a set of simple agent-based models and benefited from lessons learned in previous related projects and case studies of human response to climate change and environmental scarcity. Our approach was to define a qualitative model using extremely simple cellular agent models akin to Lovelock's Daisyworld and Schelling's segregation model. Such models do not require significant computing resources, and users can modify behavior rules to gain insights. One of the difficulties in agent-based modeling is finding the right balance between model simplicity and real-world representation. Our approach was to keep agent behaviors as simple as possible during the development stage (described herein) and to ground them with a realistic geospatial Earth system model in subsequent years. This work is directed toward incorporating projected climate data--including various C02 scenarios from the Intergovernmental Panel on Climate Change (IPCC) Third Assessment Report--and ultimately toward coupling a useful agent-based model to a general circulation model.3

Heterogeneous agent model and numerical analysis of learning

Czech Academy of Sciences Publication Activity Database

Vošvrda, Miloslav; Vácha, Lukáš

2002-01-01

Roč. 9, č. 17 (2002), s. 15-22 ISSN 1212-074X R&D Projects: GA ČR GA402/01/0034; GA ČR GA402/01/0539; GA AV ČR IAA7075202 Institutional research plan: CEZ:AV0Z1075907 Keywords : efficient markets hypothesis * technical trading rules * numerical analysis of learning Subject RIV: AH - Economics

Reinforcement Learning for Ramp Control: An Analysis of Learning Parameters

Directory of Open Access Journals (Sweden)

Chao Lu

2016-08-01

Full Text Available Reinforcement Learning (RL has been proposed to deal with ramp control problems under dynamic traffic conditions; however, there is a lack of sufficient research on the behaviour and impacts of different learning parameters. This paper describes a ramp control agent based on the RL mechanism and thoroughly analyzed the influence of three learning parameters; namely, learning rate, discount rate and action selection parameter on the algorithm performance. Two indices for the learning speed and convergence stability were used to measure the algorithm performance, based on which a series of simulation-based experiments were designed and conducted by using a macroscopic traffic flow model. Simulation results showed that, compared with the discount rate, the learning rate and action selection parameter made more remarkable impacts on the algorithm performance. Based on the analysis, some suggestionsabout how to select suitable parameter values that can achieve a superior performance were provided.

A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning.

Directory of Open Access Journals (Sweden)

Michael Jae-Yoon Chung

Full Text Available A fundamental challenge in robotics today is building robots that can learn new skills by observing humans and imitating human actions. We propose a new Bayesian approach to robotic learning by imitation inspired by the developmental hypothesis that children use self-experience to bootstrap the process of intention recognition and goal-based imitation. Our approach allows an autonomous agent to: (i learn probabilistic models of actions through self-discovery and experience, (ii utilize these learned models for inferring the goals of human actions, and (iii perform goal-based imitation for robotic learning and human-robot collaboration. Such an approach allows a robot to leverage its increasing repertoire of learned behaviors to interpret increasingly complex human actions and use the inferred goals for imitation, even when the robot has very different actuators from humans. We demonstrate our approach using two different scenarios: (i a simulated robot that learns human-like gaze following behavior, and (ii a robot that learns to imitate human actions in a tabletop organization task. In both cases, the agent learns a probabilistic model of its own actions, and uses this model for goal inference and goal-based imitation. We also show that the robotic agent can use its probabilistic model to seek human assistance when it recognizes that its inferred actions are too uncertain, risky, or impossible to perform, thereby opening the door to human-robot collaboration.

Agent Collaborative Target Localization and Classification in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Sheng Wang

2007-07-01

Full Text Available Wireless sensor networks (WSNs are autonomous networks that have beenfrequently deployed to collaboratively perform target localization and classification tasks.Their autonomous and collaborative features resemble the characteristics of agents. Suchsimilarities inspire the development of heterogeneous agent architecture for WSN in thispaper. The proposed agent architecture views WSN as multi-agent systems and mobileagents are employed to reduce in-network communication. According to the architecture,an energy based acoustic localization algorithm is proposed. In localization, estimate oftarget location is obtained by steepest descent search. The search algorithm adapts tomeasurement environments by dynamically adjusting its termination condition. With theagent architecture, target classification is accomplished by distributed support vectormachine (SVM. Mobile agents are employed for feature extraction and distributed SVMlearning to reduce communication load. Desirable learning performance is guaranteed bycombining support vectors and convex hull vectors. Fusion algorithms are designed tomerge SVM classification decisions made from various modalities. Real world experimentswith MICAz sensor nodes are conducted for vehicle localization and classification.Experimental results show the proposed agent architecture remarkably facilitates WSNdesigns and algorithm implementation. The localization and classification algorithms alsoprove to be accurate and energy efficient.

Product Distribution Theory for Control of Multi-Agent Systems

Science.gov (United States)

Lee, Chia Fan; Wolpert, David H.

2004-01-01

Product Distribution (PD) theory is a new framework for controlling Multi-Agent Systems (MAS's). First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (probability distribution of) the joint stare of the agents. Accordingly we can consider a team game in which the shared utility is a performance measure of the behavior of the MAS. For such a scenario the game is at equilibrium - the Lagrangian is optimized - when the joint distribution of the agents optimizes the system's expected performance. One common way to find that equilibrium is to have each agent run a reinforcement learning algorithm. Here we investigate the alternative of exploiting PD theory to run gradient descent on the Lagrangian. We present computer experiments validating some of the predictions of PD theory for how best to do that gradient descent. We also demonstrate how PD theory can improve performance even when we are not allowed to rerun the MAS from different initial conditions, a requirement implicit in some previous work.

Optimal Wonderful Life Utility Functions in Multi-Agent Systems

Science.gov (United States)

Wolpert, David H.; Tumer, Kagan; Swanson, Keith (Technical Monitor)

2000-01-01

The mathematics of Collective Intelligence (COINs) is concerned with the design of multi-agent systems so as to optimize an overall global utility function when those systems lack centralized communication and control. Typically in COINs each agent runs a distinct Reinforcement Learning (RL) algorithm, so that much of the design problem reduces to how best to initialize/update each agent's private utility function, as far as the ensuing value of the global utility is concerned. Traditional team game solutions to this problem assign to each agent the global utility as its private utility function. In previous work we used the COIN framework to derive the alternative Wonderful Life Utility (WLU), and experimentally established that having the agents use it induces global utility performance up to orders of magnitude superior to that induced by use of the team game utility. The WLU has a free parameter (the clamping parameter) which we simply set to zero in that previous work. Here we derive the optimal value of the clamping parameter, and demonstrate experimentally that using that optimal value can result in significantly improved performance over that of clamping to zero, over and above the improvement beyond traditional approaches.

Cognitive conflict without explicit conflict monitoring in a dynamical agent.

Science.gov (United States)

Ward, Robert; Ward, Ronnie

2006-11-01

We examine mechanisms for resolving cognitive conflict in an embodied, situated, and dynamic agent, developed through an evolutionary learning process. The agent was required to solve problems of response conflict in a dual-target "catching" task, focusing response on one of the targets while ignoring the other. Conflict in the agent was revealed at the behavioral level in terms of increased latencies to the second target. This behavioral interference was correlated to peak violations of the network's stable state equation. At the level of the agent's neural network, peak violations were also correlated to periods of disagreement in source inputs to the agent's motor effectors. Despite observing conflict at these numerous levels, we did not find any explicit conflict monitoring mechanisms within the agent. We instead found evidence of a distributed conflict management system, characterized by competitive sources within the network. In contrast to the conflict monitoring hypothesis [Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108(3), 624-652], this agent demonstrates that resolution of cognitive conflict does not require explicit conflict monitoring. We consider the implications of our results for the conflict monitoring hypothesis.

A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents.

Science.gov (United States)

Goldschmidt, Dennis; Manoonpong, Poramate; Dasgupta, Sakyasingha

2017-01-01

Despite their small size, insect brains are able to produce robust and efficient navigation in complex environments. Specifically in social insects, such as ants and bees, these navigational capabilities are guided by orientation directing vectors generated by a process called path integration. During this process, they integrate compass and odometric cues to estimate their current location as a vector, called the home vector for guiding them back home on a straight path. They further acquire and retrieve path integration-based vector memories globally to the nest or based on visual landmarks. Although existing computational models reproduced similar behaviors, a neurocomputational model of vector navigation including the acquisition of vector representations has not been described before. Here we present a model of neural mechanisms in a modular closed-loop control-enabling vector navigation in artificial agents. The model consists of a path integration mechanism, reward-modulated global learning, random search, and action selection. The path integration mechanism integrates compass and odometric cues to compute a vectorial representation of the agent's current location as neural activity patterns in circular arrays. A reward-modulated learning rule enables the acquisition of vector memories by associating the local food reward with the path integration state. A motor output is computed based on the combination of vector memories and random exploration. In simulation, we show that the neural mechanisms enable robust homing and localization, even in the presence of external sensory noise. The proposed learning rules lead to goal-directed navigation and route formation performed under realistic conditions. Consequently, we provide a novel approach for vector learning and navigation in a simulated, situated agent linking behavioral observations to their possible underlying neural substrates.

Reusable Reinforcement Learning via Shallow Trails.

Science.gov (United States)

Yu, Yang; Chen, Shi-Yong; Da, Qing; Zhou, Zhi-Hua

2018-06-01

Reinforcement learning has shown great success in helping learning agents accomplish tasks autonomously from environment interactions. Meanwhile in many real-world applications, an agent needs to accomplish not only a fixed task but also a range of tasks. For this goal, an agent can learn a metapolicy over a set of training tasks that are drawn from an underlying distribution. By maximizing the total reward summed over all the training tasks, the metapolicy can then be reused in accomplishing test tasks from the same distribution. However, in practice, we face two major obstacles to train and reuse metapolicies well. First, how to identify tasks that are unrelated or even opposite with each other, in order to avoid their mutual interference in the training. Second, how to characterize task features, according to which a metapolicy can be reused. In this paper, we propose the MetA-Policy LEarning (MAPLE) approach that overcomes the two difficulties by introducing the shallow trail. It probes a task by running a roughly trained policy. Using the rewards of the shallow trail, MAPLE automatically groups similar tasks. Moreover, when the task parameters are unknown, the rewards of the shallow trail also serve as task features. Empirical studies on several controlling tasks verify that MAPLE can train metapolicies well and receives high reward on test tasks.

Learning Based on CC1 and CC4 Neural Networks

OpenAIRE

Kak, Subhash

2017-01-01

We propose that a general learning system should have three kinds of agents corresponding to sensory, short-term, and long-term memory that implicitly will facilitate context-free and context-sensitive aspects of learning. These three agents perform mututally complementary functions that capture aspects of the human cognition system. We investigate the use of CC1 and CC4 networks for use as models of short-term and sensory memory.

Agents, Bayes, and Climatic Risks - a modular modelling approach

Directory of Open Access Journals (Sweden)

A. Haas

2005-01-01

Full Text Available When insurance firms, energy companies, governments, NGOs, and other agents strive to manage climatic risks, it is by no way clear what the aggregate outcome should and will be. As a framework for investigating this subject, we present the LAGOM model family. It is based on modules depicting learning social agents. For managing climate risks, our agents use second order probabilities and update them by means of a Bayesian mechanism while differing in priors and risk aversion. The interactions between these modules and the aggregate outcomes of their actions are implemented using further modules. The software system is implemented as a series of parallel processes using the CIAMn approach. It is possible to couple modules irrespective of the language they are written in, the operating system under which they are run, and the physical location of the machine.

Agents, Bayes, and Climatic Risks - a modular modelling approach

Science.gov (United States)

Haas, A.; Jaeger, C.

2005-08-01

When insurance firms, energy companies, governments, NGOs, and other agents strive to manage climatic risks, it is by no way clear what the aggregate outcome should and will be. As a framework for investigating this subject, we present the LAGOM model family. It is based on modules depicting learning social agents. For managing climate risks, our agents use second order probabilities and update them by means of a Bayesian mechanism while differing in priors and risk aversion. The interactions between these modules and the aggregate outcomes of their actions are implemented using further modules. The software system is implemented as a series of parallel processes using the CIAMn approach. It is possible to couple modules irrespective of the language they are written in, the operating system under which they are run, and the physical location of the machine.

Heterogeneous Agent Model with Memory and Asset Price Behaviour

Czech Academy of Sciences Publication Activity Database

Vošvrda, Miloslav; Vácha, Lukáš

2003-01-01

Roč. 12, č. 2 (2003), s. 155-168 ISSN 1210-0455 R&D Projects: GA ČR GA402/00/0439; GA ČR GA402/01/0034 Institutional research plan: CEZ:AV0Z1075907 Keywords : efficient markets hypothesis * technical trading rules * heterogeneous agent model with memory and learning Subject RIV: AH - Economics

A Graphical Evolutionary Game Approach to Social Learning

Science.gov (United States)

Cao, Xuanyu; Liu, K. J. Ray

2017-06-01

In this work, we study the social learning problem, in which agents of a networked system collaborate to detect the state of the nature based on their private signals. A novel distributed graphical evolutionary game theoretic learning method is proposed. In the proposed game-theoretic method, agents only need to communicate their binary decisions rather than the real-valued beliefs with their neighbors, which endows the method with low communication complexity. Under mean field approximations, we theoretically analyze the steady state equilibria of the game and show that the evolutionarily stable states (ESSs) coincide with the decisions of the benchmark centralized detector. Numerical experiments are implemented to confirm the effectiveness of the proposed game-theoretic learning method.

Ethnopedagogy: Culturally Contextualised Learning and Teaching as an Agent of Change

Science.gov (United States)

Dunbar-Hall, Peter

2009-01-01

Lucy Green's latest book, "Music, Informal Learning and the School: A New Classroom Pedagogy" (Green, 2008) posits that the learning taking place among popular musicians, developed out of a need to create and perform pieces of music, and found "everywhere in everyday life" rather than in the formalised settings of the majority…

Stochastic abstract policies: generalizing knowledge to improve reinforcement learning.

Science.gov (United States)

Koga, Marcelo L; Freire, Valdinei; Costa, Anna H R

2015-01-01

Reinforcement learning (RL) enables an agent to learn behavior by acquiring experience through trial-and-error interactions with a dynamic environment. However, knowledge is usually built from scratch and learning to behave may take a long time. Here, we improve the learning performance by leveraging prior knowledge; that is, the learner shows proper behavior from the beginning of a target task, using the knowledge from a set of known, previously solved, source tasks. In this paper, we argue that building stochastic abstract policies that generalize over past experiences is an effective way to provide such improvement and this generalization outperforms the current practice of using a library of policies. We achieve that contributing with a new algorithm, AbsProb-PI-multiple and a framework for transferring knowledge represented as a stochastic abstract policy in new RL tasks. Stochastic abstract policies offer an effective way to encode knowledge because the abstraction they provide not only generalizes solutions but also facilitates extracting the similarities among tasks. We perform experiments in a robotic navigation environment and analyze the agent's behavior throughout the learning process and also assess the transfer ratio for different amounts of source tasks. We compare our method with the transfer of a library of policies, and experiments show that the use of a generalized policy produces better results by more effectively guiding the agent when learning a target task.

Dimensions of Usability: Cougaar, Aglets and Adaptive Agent Architecture (AAA)

Energy Technology Data Exchange (ETDEWEB)

Haack, Jereme N.; Cowell, Andrew J.; Gorton, Ian

2004-06-20

Research and development organizations are constantly evaluating new technologies in order to implement the next generation of advanced applications. At Pacific Northwest National Laboratory, agent technologies are perceived as an approach that can provide a competitive advantage in the construction of highly sophisticated software systems in a range of application areas. An important factor in selecting a successful agent architecture is the level of support it provides the developer in respect to developer support, examples of use, integration into current workflow and community support. Without such assistance, the developer must invest more effort into learning instead of applying the technology. Like many other applied research organizations, our staff are not dedicated to a single project and must acquire new skills as required, underlining the importance of being able to quickly become proficient. A project was instigated to evaluate three candidate agent toolkits across the dimensions of support they provide. This paper reports on the outcomes of this evaluation and provides insights into the agent technologies evaluated.

The Study of Reinforcement Learning for Traffic Self-Adaptive Control under Multiagent Markov Game Environment

Directory of Open Access Journals (Sweden)

Lun-Hui Xu

2013-01-01

Full Text Available Urban traffic self-adaptive control problem is dynamic and uncertain, so the states of traffic environment are hard to be observed. Efficient agent which controls a single intersection can be discovered automatically via multiagent reinforcement learning. However, in the majority of the previous works on this approach, each agent needed perfect observed information when interacting with the environment and learned individually with less efficient coordination. This study casts traffic self-adaptive control as a multiagent Markov game problem. The design employs traffic signal control agent (TSCA for each signalized intersection that coordinates with neighboring TSCAs. A mathematical model for TSCAs’ interaction is built based on nonzero-sum markov game which has been applied to let TSCAs learn how to cooperate. A multiagent Markov game reinforcement learning approach is constructed on the basis of single-agent Q-learning. This method lets each TSCA learn to update its Q-values under the joint actions and imperfect information. The convergence of the proposed algorithm is analyzed theoretically. The simulation results show that the proposed method is convergent and effective in realistic traffic self-adaptive control setting.

Constructing and Refining Knowledge Bases: A Collaborative Apprenticeship Multistrategy Learning Approach

National Research Council Canada - National Science Library

Tecuci, Gheorghe

2000-01-01

This research has developed a theory, methodology and learning agent shell for development of knowledge bases and knowledge-based agents, by domain experts, with limited assistance from knowledge engineers...

The value of less connected agents in Boolean networks

Science.gov (United States)

Epstein, Daniel; Bazzan, Ana L. C.

2013-11-01

In multiagent systems, agents often face binary decisions where one seeks to take either the minority or the majority side. Examples are minority and congestion games in general, i.e., situations that require coordination among the agents in order to depict efficient decisions. In minority games such as the El Farol Bar Problem, previous works have shown that agents may reach appropriate levels of coordination, mostly by looking at the history of past decisions. Not many works consider any kind of structure of the social network, i.e., how agents are connected. Moreover, when structure is indeed considered, it assumes some kind of random network with a given, fixed connectivity degree. The present paper departs from the conventional approach in some ways. First, it considers more realistic network topologies, based on preferential attachments. This is especially useful in social networks. Second, the formalism of random Boolean networks is used to help agents to make decisions given their attachments (for example acquaintances). This is coupled with a reinforcement learning mechanism that allows agents to select strategies that are locally and globally efficient. Third, we use agent-based modeling and simulation, a microscopic approach, which allows us to draw conclusions about individuals and/or classes of individuals. Finally, for the sake of illustration we use two different scenarios, namely the El Farol Bar Problem and a binary route choice scenario. With this approach we target systems that adapt dynamically to changes in the environment, including other adaptive decision-makers. Our results using preferential attachments and random Boolean networks are threefold. First we show that an efficient equilibrium can be achieved, provided agents do experimentation. Second, microscopic analysis show that influential agents tend to consider few inputs in their Boolean functions. Third, we have also conducted measurements related to network clustering and centrality

Fast social-like learning of complex behaviors based on motor motifs

Science.gov (United States)

Calvo Tapia, Carlos; Tyukin, Ivan Y.; Makarov, Valeri A.

2018-05-01

Social learning is widely observed in many species. Less experienced agents copy successful behaviors exhibited by more experienced individuals. Nevertheless, the dynamical mechanisms behind this process remain largely unknown. Here we assume that a complex behavior can be decomposed into a sequence of n motor motifs. Then a neural network capable of activating motor motifs in a given sequence can drive an agent. To account for (n -1 )! possible sequences of motifs in a neural network, we employ the winnerless competition approach. We then consider a teacher-learner situation: one agent exhibits a complex movement, while another one aims at mimicking the teacher's behavior. Despite the huge variety of possible motif sequences we show that the learner, equipped with the provided learning model, can rewire "on the fly" its synaptic couplings in no more than (n -1 ) learning cycles and converge exponentially to the durations of the teacher's motifs. We validate the learning model on mobile robots. Experimental results show that the learner is indeed capable of copying the teacher's behavior composed of six motor motifs in a few learning cycles. The reported mechanism of learning is general and can be used for replicating different functions, including, for example, sound patterns or speech.

Coupled replicator equations for the dynamics of learning in multiagent systems

Science.gov (United States)

Sato, Yuzuru; Crutchfield, James P.

2003-01-01

Starting with a group of reinforcement-learning agents we derive coupled replicator equations that describe the dynamics of collective learning in multiagent systems. We show that, although agents model their environment in a self-interested way without sharing knowledge, a game dynamics emerges naturally through environment-mediated interactions. An application to rock-scissors-paper game interactions shows that the collective learning dynamics exhibits a diversity of competitive and cooperative behaviors. These include quasiperiodicity, stable limit cycles, intermittency, and deterministic chaos—behaviors that should be expected in heterogeneous multiagent systems described by the general replicator equations we derive.

Intelligent Agent Based Traffic Signal Control on Isolated Intersections

Directory of Open Access Journals (Sweden)

Daniela Koltovska

2014-08-01

Full Text Available The purpose of this paper is to develop an adaptive signal control strategy on isolated urban intersections. An innovative approach to defining the set of states dependent on the actual and primarily observed parameters has been introduced. ?he Q–learning algorithm has been applied. The developed self-learning adaptive signal strategy has been tested on a re?l intersection. The intelligent agent results have been compared to those in cases of fixed-time and actuated control. Regarding the average total delay, the total number of stops and the total throughput, the best results have been obtained for unknown traffic demand and over-capacity.

Learning Negotiation Policies Using IB3 and Bayesian Networks

Science.gov (United States)

Nalepa, Gislaine M.; Ávila, Bráulio C.; Enembreck, Fabrício; Scalabrin, Edson E.

This paper presents an intelligent offer policy in a negotiation environment, in which each agent involved learns the preferences of its opponent in order to improve its own performance. Each agent must also be able to detect drifts in the opponent's preferences so as to quickly adjust itself to their new offer policy. For this purpose, two simple learning techniques were first evaluated: (i) based on instances (IB3) and (ii) based on Bayesian Networks. Additionally, as its known that in theory group learning produces better results than individual/single learning, the efficiency of IB3 and Bayesian classifier groups were also analyzed. Finally, each decision model was evaluated in moments of concept drift, being the drift gradual, moderate or abrupt. Results showed that both groups of classifiers were able to effectively detect drifts in the opponent's preferences.

Best Response Bayesian Reinforcement Learning for Multiagent Systems with State Uncertainty

NARCIS (Netherlands)

Oliehoek, F.A.; Amato, C.

2014-01-01

It is often assumed that agents in multiagent systems with state uncertainty have full knowledge of the model of dy- namics and sensors, but in many cases this is not feasible. A more realistic assumption is that agents must learn about the environment and other agents while acting. Bayesian methods

Analysis of the “naming game” with learning errors in communications

OpenAIRE

Yang Lou; Guanrong Chen

2015-01-01

Naming game simulates the process of naming an objective by a population of agents organized in a certain communication network. By pair-wise iterative interactions, the population reaches consensus asymptotically. We study naming game with communication errors during pair-wise conversations, with error rates in a uniform probability distribution. First, a model of naming game with learning errors in communications (NGLE) is proposed. Then, a strategy for agents to prevent learning errors is ...

Place preference and vocal learning rely on distinct reinforcers in songbirds.

Science.gov (United States)

Murdoch, Don; Chen, Ruidong; Goldberg, Jesse H

2018-04-30

In reinforcement learning (RL) agents are typically tasked with maximizing a single objective function such as reward. But it remains poorly understood how agents might pursue distinct objectives at once. In machines, multiobjective RL can be achieved by dividing a single agent into multiple sub-agents, each of which is shaped by agent-specific reinforcement, but it remains unknown if animals adopt this strategy. Here we use songbirds to test if navigation and singing, two behaviors with distinct objectives, can be differentially reinforced. We demonstrate that strobe flashes aversively condition place preference but not song syllables. Brief noise bursts aversively condition song syllables but positively reinforce place preference. Thus distinct behavior-generating systems, or agencies, within a single animal can be shaped by correspondingly distinct reinforcement signals. Our findings suggest that spatially segregated vocal circuits can solve a credit assignment problem associated with multiobjective learning.

Knowledge models as agents of meaninful learning and knowledge creation.

Directory of Open Access Journals (Sweden)

Fermín María González García

2013-08-01

Full Text Available 0 0 1 172 952 USAL 7 2 1122 14.0 Normal 0 21 false false false ES JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-ansi-language:ES; mso-fareast-language:EN-US;} The educational change that pushes the current context requires a shift in the unfortunately predominant positivist-behaviourist model that favours mechanical      memoristic learning, ideal breeding ground for the existence and maintenance of conceptual errors, to another cognitive-constructivist that stimulates meaningful learning to allow students to build and master knowledge, therefore to be more creative and critical. We present here a model of knowledge where students construct new knowledge as a result of significant learning. Students play an active role, learning not only about the product, but also about the process itself (meta-cognition. We also show how to promote teacher activity primarily in order to create the conditions that facilitate the student to transform the information in useful, substantive knowledge, to be  incorporated in his knowledge structure and in his long-term memory. Finally, we provide elements to measure what the student knows and to assess how their cognitive structure has changed regarding their ancient knowledge; that is, to assess the necessary conceptual change.

The structure of intelligent agent projecting and the possibilities of its execution

Directory of Open Access Journals (Sweden)

Yefremov M.F.

2017-04-01

Full Text Available Most of today’s information systems are not designed for independent decision making, behavior of such systems should be incorporated at the design stage. Entering the conditions that are not included by system developers, lead to a crash. The exponential growth of computing power of modern processors leads to an increase in range of tasks that can be automated, and an increase in their complexity. Partially testing helps to solve the problem, but it has a drawback, precisely the behavior of the system is usually tested in conditions which are already taken into account at the design stage. One approach to address this problem is the agent-oriented programming and the use of multi-agent systems. The aim of the work is to highlight contemporary approaches: a definition of agent concepts, applications of multi-agent systems, mathematical model agent, modern methods of design and implementation of intelligent agents, also covered approaches to the definition of the concept of an intelligent agent, the application of intelligent agents and multi-agent systems, and methods theoretically agent and describe its implementation. The basis of these methods of abstract description of the intelligent agent is the works of M.Wooldridge and N.Jennings. We propose several methods to describe such behaviors of intelligent agent as self-learning, goal setting and forecasting, and planning. Therefore we cover the concept of «intent» and widespread mental (beliefs-desires-intentions BDI architecture of intelligent agent, as well as the proposed methods of their description in the framework of the developed formalism.

Autonomous Learning from a Social Cognitive Perspective

Science.gov (United States)

Ponton, Michael K.; Rhea, Nancy E.

2006-01-01

The current perspective of autonomous learning defines it as the agentive exhibition of resourcefulness, initiative, and persistence in self-directed learning. As a form of human agency, it has been argued in the literature that this perspective should be consistent with Bandura's (1986) Social Cognitive Theory (SCT). The purpose of this article…

Blackboxing: social learning strategies and cultural evolution.

Science.gov (United States)

Heyes, Cecilia

2016-05-05

Social learning strategies (SLSs) enable humans, non-human animals, and artificial agents to make adaptive decisions aboutwhenthey should copy other agents, andwhothey should copy. Behavioural ecologists and economists have discovered an impressive range of SLSs, and explored their likely impact on behavioural efficiency and reproductive fitness while using the 'phenotypic gambit'; ignoring, or remaining deliberately agnostic about, the nature and origins of the cognitive processes that implement SLSs. Here I argue that this 'blackboxing' of SLSs is no longer a viable scientific strategy. It has contributed, through the 'social learning strategies tournament', to the premature conclusion that social learning is generally better than asocial learning, and to a deep puzzle about the relationship between SLSs and cultural evolution. The puzzle can be solved by recognizing that whereas most SLSs are 'planetary'--they depend on domain-general cognitive processes--some SLSs, found only in humans, are 'cook-like'--they depend on explicit, metacognitive rules, such ascopy digital natives. These metacognitive SLSs contribute to cultural evolution by fostering the development of processes that enhance the exclusivity, specificity, and accuracy of social learning. © 2016 The Author(s).

Blackboxing: social learning strategies and cultural evolution

Science.gov (United States)

Heyes, Cecilia

2016-01-01

Social learning strategies (SLSs) enable humans, non-human animals, and artificial agents to make adaptive decisions about when they should copy other agents, and who they should copy. Behavioural ecologists and economists have discovered an impressive range of SLSs, and explored their likely impact on behavioural efficiency and reproductive fitness while using the ‘phenotypic gambit’; ignoring, or remaining deliberately agnostic about, the nature and origins of the cognitive processes that implement SLSs. Here I argue that this ‘blackboxing' of SLSs is no longer a viable scientific strategy. It has contributed, through the ‘social learning strategies tournament', to the premature conclusion that social learning is generally better than asocial learning, and to a deep puzzle about the relationship between SLSs and cultural evolution. The puzzle can be solved by recognizing that whereas most SLSs are ‘planetary'—they depend on domain-general cognitive processes—some SLSs, found only in humans, are ‘cook-like'—they depend on explicit, metacognitive rules, such as copy digital natives. These metacognitive SLSs contribute to cultural evolution by fostering the development of processes that enhance the exclusivity, specificity, and accuracy of social learning. PMID:27069046

Farmer, Agent, and Specialist Perspectives on Preferences for Learning among Today's Farmers

Science.gov (United States)

Franz, Nancy K.; Piercy, Fred; Donaldson, Joseph; Westbrook, Johnnie; Richard, Robert

2010-01-01

Few studies have examined the types of educational delivery methods preferred by farmers (Eckert & Bell, 2005; Eckert & Bell, 2006). The research project reported here explored the preferred learning methods of farmers in Louisiana, Tennessee, and Virginia. Data on learning methods collected directly from farmers were compared with…

AgPi: Agents on Raspberry Pi

Directory of Open Access Journals (Sweden)

Tushar Semwal

2016-10-01

Full Text Available The Raspberry Pi and its variants have brought with them an aura of change in the world of embedded systems. With their impressive computation and communication capabilities and low footprint, these devices have thrown open the possibility of realizing a network of things in a very cost-effective manner. While such networks offer good solutions to prominent issues, they are indeed a long way from being smart or intelligent. Most of the currently available implementations of such a network of devices involve a centralized cloud-based server that contributes to making the necessary intelligent decisions, leaving these devices fairly underutilized. Though this paradigm provides for an easy and rapid solution, they have limited scalability, are less robust and at times prove to be expensive. In this paper, we introduce the concept of Agents on Raspberry Pi (AgPi as a cyber solution to enhance the smartness and flexibility of such embedded networks of physical devices in a decentralized manner. The use of a Multi-Agent System (MAS running on Raspberry Pis aids agents, both static and mobile, to govern the various activities within the network. Agents can act autonomously or on behalf of a human user and can collaborate, learn, adapt and act, thus contributing to embedded intelligence. This paper describes how Tartarus, a multi-agent platform, embedded on Raspberry Pis that constitute a network, can bring the best out of the system. To reveal the versatility of the concept of AgPi, an application for a Location-Aware and Tracking Service (LATS is presented. The results obtained from a comparison of data transfer cost between the conventional cloud-based approach with AgPi have also been included.

Smart Aerospace eCommerce: Using Intelligent Agents in a NASA Mission Services Ordering Application

Science.gov (United States)

Moleski, Walt; Luczak, Ed; Morris, Kim; Clayton, Bill; Scherf, Patricia; Obenschain, Arthur F. (Technical Monitor)

2002-01-01

This paper describes how intelligent agent technology was successfully prototyped and then deployed in a smart eCommerce application for NASA. An intelligent software agent called the Intelligent Service Validation Agent (ISVA) was added to an existing web-based ordering application to validate complex orders for spacecraft mission services. This integration of intelligent agent technology with conventional web technology satisfies an immediate NASA need to reduce manual order processing costs. The ISVA agent checks orders for completeness, consistency, and correctness, and notifies users of detected problems. ISVA uses NASA business rules and a knowledge base of NASA services, and is implemented using the Java Expert System Shell (Jess), a fast rule-based inference engine. The paper discusses the design of the agent and knowledge base, and the prototyping and deployment approach. It also discusses future directions and other applications, and discusses lessons-learned that may help other projects make their aerospace eCommerce applications smarter.

Nintendo Super Smash Bros. Melee: An "Untouchable" Agent

OpenAIRE

Parr, Ben; Dilipkumar, Deepak; Liu, Yuan

2017-01-01

Nintendo's Super Smash Bros. Melee fighting game can be emulated on modern hardware allowing us to inspect internal memory states, such as character positions. We created an AI that avoids being hit by training using these internal memory states and outputting controller button presses. After training on a month's worth of Melee matches, our best agent learned to avoid the toughest AI built into the game for a full minute 74.6% of the time.

Collective learning for the emergence of social norms in networked multiagent systems.

Science.gov (United States)

Yu, Chao; Zhang, Minjie; Ren, Fenghui

2014-12-01

Social norms such as social rules and conventions play a pivotal role in sustaining system order by regulating and controlling individual behaviors toward a global consensus in large-scale distributed systems. Systematic studies of efficient mechanisms that can facilitate the emergence of social norms enable us to build and design robust distributed systems, such as electronic institutions and norm-governed sensor networks. This paper studies the emergence of social norms via learning from repeated local interactions in networked multiagent systems. A collective learning framework, which imitates the opinion aggregation process in human decision making, is proposed to study the impact of agent local collective behaviors on the emergence of social norms in a number of different situations. In the framework, each agent interacts repeatedly with all of its neighbors. At each step, an agent first takes a best-response action toward each of its neighbors and then combines all of these actions into a final action using ensemble learning methods. Extensive experiments are carried out to evaluate the framework with respect to different network topologies, learning strategies, numbers of actions, influences of nonlearning agents, and so on. Experimental results reveal some significant insights into the manipulation and control of norm emergence in networked multiagent systems achieved through local collective behaviors.

Combining human and machine intelligence to derive agents' behavioral rules for groundwater irrigation

Science.gov (United States)

Hu, Yao; Quinn, Christopher J.; Cai, Ximing; Garfinkle, Noah W.

2017-11-01

For agent-based modeling, the major challenges in deriving agents' behavioral rules arise from agents' bounded rationality and data scarcity. This study proposes a "gray box" approach to address the challenge by incorporating expert domain knowledge (i.e., human intelligence) with machine learning techniques (i.e., machine intelligence). Specifically, we propose using directed information graph (DIG), boosted regression trees (BRT), and domain knowledge to infer causal factors and identify behavioral rules from data. A case study is conducted to investigate farmers' pumping behavior in the Midwest, U.S.A. Results show that four factors identified by the DIG algorithm- corn price, underlying groundwater level, monthly mean temperature and precipitation- have main causal influences on agents' decisions on monthly groundwater irrigation depth. The agent-based model is then developed based on the behavioral rules represented by three DIGs and modeled by BRTs, and coupled with a physically-based groundwater model to investigate the impacts of agents' pumping behavior on the underlying groundwater system in the context of coupled human and environmental systems.

Spike-based decision learning of Nash equilibria in two-player games.

Directory of Open Access Journals (Sweden)

Johannes Friedrich

Full Text Available Humans and animals face decision tasks in an uncertain multi-agent environment where an agent's strategy may change in time due to the co-adaptation of others strategies. The neuronal substrate and the computational algorithms underlying such adaptive decision making, however, is largely unknown. We propose a population coding model of spiking neurons with a policy gradient procedure that successfully acquires optimal strategies for classical game-theoretical tasks. The suggested population reinforcement learning reproduces data from human behavioral experiments for the blackjack and the inspector game. It performs optimally according to a pure (deterministic and mixed (stochastic Nash equilibrium, respectively. In contrast, temporal-difference(TD-learning, covariance-learning, and basic reinforcement learning fail to perform optimally for the stochastic strategy. Spike-based population reinforcement learning, shown to follow the stochastic reward gradient, is therefore a viable candidate to explain automated decision learning of a Nash equilibrium in two-player games.

An adaptive multi-agent-based approach to smart grids control and optimization

Energy Technology Data Exchange (ETDEWEB)

Carvalho, Marco [Florida Institute of Technology, Melbourne, FL (United States); Perez, Carlos; Granados, Adrian [Institute for Human and Machine Cognition, Ocala, FL (United States)

2012-03-15

In this paper, we describe a reinforcement learning-based approach to power management in smart grids. The scenarios we consider are smart grid settings where renewable power sources (e.g. Photovoltaic panels) have unpredictable variations in power output due, for example, to weather or cloud transient effects. Our approach builds on a multi-agent system (MAS)-based infrastructure for the monitoring and coordination of smart grid environments with renewable power sources and configurable energy storage devices (battery banks). Software agents are responsible for tracking and reporting power flow variations at different points in the grid, and to optimally coordinate the engagement of battery banks (i.e. charge/idle/discharge modes) to maintain energy requirements to end-users. Agents are able to share information and coordinate control actions through a parallel communications infrastructure, and are also capable of learning, from experience, how to improve their response strategies for different operational conditions. In this paper we describe our approach and address some of the challenges associated with the communications infrastructure for distributed coordination. We also present some preliminary results of our first simulations using the GridLAB-D simulation environment, created by the US Department of Energy (DoE) at Pacific Northwest National Laboratory (PNNL). (orig.)

Intelligent agents for adaptive security market surveillance

Science.gov (United States)

Chen, Kun; Li, Xin; Xu, Baoxun; Yan, Jiaqi; Wang, Huaiqing

2017-05-01

Market surveillance systems have increasingly gained in usage for monitoring trading activities in stock markets to maintain market integrity. Existing systems primarily focus on the numerical analysis of market activity data and generally ignore textual information. To fulfil the requirements of information-based surveillance, a multi-agent-based architecture that uses agent intercommunication and incremental learning mechanisms is proposed to provide a flexible and adaptive inspection process. A prototype system is implemented using the techniques of text mining and rule-based reasoning, among others. Based on experiments in the scalping surveillance scenario, the system can identify target information evidence up to 87.50% of the time and automatically identify 70.59% of cases depending on the constraints on the available information sources. The results of this study indicate that the proposed information surveillance system is effective. This study thus contributes to the market surveillance literature and has significant practical implications.

African Logistics Agents and Middlemen as Cultural Brokers in Guangzhou

Directory of Open Access Journals (Sweden)

Gordon Mathews

2015-01-01

Full Text Available This article begins by asking how African traders learn to adjust to the foreign world of Guangzhou, China, and suggests that African logistics agents and middlemen serve as cultural brokers for these traders. After defining “cultural broker” and discussing why these brokers are not usually Chinese, it explores this role as played by ten logistics agents/middlemen from Kenya, Nigeria, Ghana and the Democratic Republic of the Congo. As logistics agents, these people help their customers in practically adjusting to Chinese life, and as middlemen they serve to grease the wheels of commerce between African customers and Chinese suppliers. This is despite their own ambivalent views of China as a place to live. They play an essential role in enabling harmonious relations between Africans and Chinese in Guangzhou, even though they see themselves not as cultural brokers but simply as businessmen.

Design Principles of an Open Agent Architecture for Web-Based Learning Community.

Science.gov (United States)

Jin, Qun; Ma, Jianhua; Huang, Runhe; Shih, Timothy K.

A Web-based learning community involves much more than putting learning materials into a Web site. It can be seen as a complex virtual organization involved with people, facilities, and cyber-environment. Tremendous work and manpower for maintaining, upgrading, and managing facilities and the cyber-environment are required. There is presented an…

Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

Science.gov (United States)

Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

2015-04-01

A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to

Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

Directory of Open Access Journals (Sweden)

Kai Olav Ellefsen

2015-04-01

Full Text Available A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand. To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1 that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2 that one benefit of the modularity ubiquitous in the brains of natural animals

Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills

Science.gov (United States)

Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

2015-01-01

A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to

Agent Programming Languages and Logics in Agent-Based Simulation

DEFF Research Database (Denmark)

Larsen, John

2018-01-01

and social behavior, and work on verification. Agent-based simulation is an approach for simulation that also uses the notion of agents. Although agent programming languages and logics are much less used in agent-based simulation, there are successful examples with agents designed according to the BDI...

A theoretical analysis of temporal difference learning in the iterated prisoner's dilemma game.

Science.gov (United States)

Masuda, Naoki; Ohtsuki, Hisashi

2009-11-01

Direct reciprocity is a chief mechanism of mutual cooperation in social dilemma. Agents cooperate if future interactions with the same opponents are highly likely. Direct reciprocity has been explored mostly by evolutionary game theory based on natural selection. Our daily experience tells, however, that real social agents including humans learn to cooperate based on experience. In this paper, we analyze a reinforcement learning model called temporal difference learning and study its performance in the iterated Prisoner's Dilemma game. Temporal difference learning is unique among a variety of learning models in that it inherently aims at increasing future payoffs, not immediate ones. It also has a neural basis. We analytically and numerically show that learners with only two internal states properly learn to cooperate with retaliatory players and to defect against unconditional cooperators and defectors. Four-state learners are more capable of achieving a high payoff against various opponents. Moreover, we numerically show that four-state learners can learn to establish mutual cooperation for sufficiently small learning rates.

Fostering Autonomy in EFL Cross-Cultural Distance Learning

Science.gov (United States)

Lee, Hikyoung

2008-01-01

The Korea Waseda Cross Cultural Distance Learning Project (KWCCDLP) is an endeavor to promote awareness of linguistic and cultural differences of speakers from different backgrounds through the medium of English. The project fully utilizes a student centered approach to learning where learners are the agents. This project aimed at university level…

Agents unleashed a public domain look at agent technology

CERN Document Server

Wayner, Peter

1995-01-01

Agents Unleashed: A Public Domain Look at Agent Technology covers details of building a secure agent realm. The book discusses the technology for creating seamlessly integrated networks that allow programs to move from machine to machine without leaving a trail of havoc; as well as the technical details of how an agent will move through the network, prove its identity, and execute its code without endangering the host. The text also describes the organization of the host's work processing an agent; error messages, bad agent expulsion, and errors in XLISP-agents; and the simulators of errors, f

The Application of Intentional Subjective Properties and Mediated Communication Tools to Software Agents in Online Disputes Resolution Environments

Directory of Open Access Journals (Sweden)

Renzo Gobbin

2004-11-01

Full Text Available This paper examines the use of subjective properties in modeling an architecture for cooperative agents using Agent Communication Language (ACL that is used as a mediating tool for cooperative communication activities between and within software agents. The role that subjective and objective properties have in explaining and modeling agent internalization and externalization of ACL messages is investigated and related to Vygotsky’s developmental learning theories such as Mediated Activity Theory. A novel agent architecture ALMA (Agent Language Mediated Activity based on the integration of agents’ subjective and objective properties within an agent communication activity framework will be presented. The relevance of software agents subjective properties in modeling applications such as e-Law Online Dispute Resolution for e-business contractual arrangements using natural language subject/object relation in their communication patterns will be discussed.

An active learning organisation: teaching projects in electrical engineering education

NARCIS (Netherlands)

Christensen, H.-P.; Vos, Henk; de Graaff, E.; Lemoult, B.

2004-01-01

The introduction of active learning in engineering education is often started by enthusiastic teachers or change agents. They usually encounter resistance from stakeholders such as colleagues, department boards or students. For a successful introduction these stakeholders all have to learn what

Lifelong Transfer Learning for Heterogeneous Teams of Agents in Sequential Decision Processes

Science.gov (United States)

2016-06-01

computational complexity and exhibits sublinear regret , thus providing strong theoretical guarantees [Bou Ammar et al., 2015b] (see Appendix C for details...transferred knowledge, providing a potential mechanism for predicting the effectiveness of transfer learning (and thereby avoiding negative transfer). One...learning from demonstration. We theoretically and empirically analyze the performance of the proposed method and derive, for the first time, regret

The "proactive" model of learning: Integrative framework for model-free and model-based reinforcement learning utilizing the associative learning-based proactive brain concept.

Science.gov (United States)

Zsuga, Judit; Biro, Klara; Papp, Csaba; Tajti, Gabor; Gesztelyi, Rudolf

2016-02-01

Reinforcement learning (RL) is a powerful concept underlying forms of associative learning governed by the use of a scalar reward signal, with learning taking place if expectations are violated. RL may be assessed using model-based and model-free approaches. Model-based reinforcement learning involves the amygdala, the hippocampus, and the orbitofrontal cortex (OFC). The model-free system involves the pedunculopontine-tegmental nucleus (PPTgN), the ventral tegmental area (VTA) and the ventral striatum (VS). Based on the functional connectivity of VS, model-free and model based RL systems center on the VS that by integrating model-free signals (received as reward prediction error) and model-based reward related input computes value. Using the concept of reinforcement learning agent we propose that the VS serves as the value function component of the RL agent. Regarding the model utilized for model-based computations we turned to the proactive brain concept, which offers an ubiquitous function for the default network based on its great functional overlap with contextual associative areas. Hence, by means of the default network the brain continuously organizes its environment into context frames enabling the formulation of analogy-based association that are turned into predictions of what to expect. The OFC integrates reward-related information into context frames upon computing reward expectation by compiling stimulus-reward and context-reward information offered by the amygdala and hippocampus, respectively. Furthermore we suggest that the integration of model-based expectations regarding reward into the value signal is further supported by the efferent of the OFC that reach structures canonical for model-free learning (e.g., the PPTgN, VTA, and VS). (c) 2016 APA, all rights reserved).

Mobile Agent Data Integrity Using Multi-Agent Architecture

National Research Council Canada - National Science Library

McDonald, Jeffrey

2004-01-01

.... Security issues for mobile agents continue to produce research interest, particularly in developing mechanisms that guarantee protection of agent data and agent computations in the presence of malicious hosts...

Optimal long-term contracting with learning

OpenAIRE

He, Zhiguo; Wei, Bin; Yu, Jianfeng; Gao, Feng

2016-01-01

We introduce uncertainty into Holmstrom and Milgrom (1987) to study optimal long-term contracting with learning. In a dynamic relationship, the agent's shirking not only reduces current performance but also increases the agent's information rent due to the persistent belief manipulation effect. We characterize the optimal contract using the dynamic programming technique in which information rent is the unique state variable. In the optimal contract, the optimal effort is front-loaded and decr...

How initial representations shape coupled learning processes

DEFF Research Database (Denmark)

Puranam, Phanish; Swamy, M.

2016-01-01

Coupled learning processes, in which specialists from different domains learn how to make interdependent choices among alternatives, are common in organizations. We explore the role played by initial representations held by the learners in coupled learning processes using a formal agent-based model....... We find that initial representations have important consequences for the success of the coupled learning process, particularly when communication is constrained and individual rates of learning are high. Under these conditions, initial representations that generate incorrect beliefs can outperform...... one that does not discriminate among alternatives, or even a mix of correct and incorrect representations among the learners. We draw implications for the design of coupled learning processes in organizations. © 2016 INFORMS....

An Agent Based approach to design Serious Game

Directory of Open Access Journals (Sweden)

Manuel Gentile

2014-06-01

Full Text Available Serious games are designed to train and educate learners, opening up new learning approaches like exploratory learning and situated cognition.  Despite growing interest in these games, their design is still an artisan process.On the basis of experiences in designing computer simulation, this paper proposes an agent-based approach to guide the design process of a serious game. The proposed methodology allows the designer to strike the right equilibrium between educational effectiveness and entertainment, realism and complexity.The design of the PNPVillage game is used as a case study. The PNPVillage game aims to introduce and foster an entrepreneurial mindset among young students. It was implemented within the framework of the European project “I  can… I cannot… I go!” Rev.2

Reinforcement learning: Solving two case studies

Science.gov (United States)

Duarte, Ana Filipa; Silva, Pedro; dos Santos, Cristina Peixoto

2012-09-01

Reinforcement Learning algorithms offer interesting features for the control of autonomous systems, such as the ability to learn from direct interaction with the environment, and the use of a simple reward signalas opposed to the input-outputs pairsused in classic supervised learning. The reward signal indicates the success of failure of the actions executed by the agent in the environment. In this work, are described RL algorithmsapplied to two case studies: the Crawler robot and the widely known inverted pendulum. We explore RL capabilities to autonomously learn a basic locomotion pattern in the Crawler, andapproach the balancing problem of biped locomotion using the inverted pendulum.

Interactive machine learning for health informatics: when do we need the human-in-the-loop?

Science.gov (United States)

Holzinger, Andreas

2016-06-01

Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as "algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human." This "human-in-the-loop" can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.

A framework for learning and planning against switching strategies in repeated games

Science.gov (United States)

Hernandez-Leal, Pablo; Munoz de Cote, Enrique; Sucar, L. Enrique

2014-04-01

Intelligent agents, human or artificial, often change their behaviour as they interact with other agents. For an agent to optimise its performance when interacting with such agents, it must be capable of detecting and adapting according to such changes. This work presents an approach on how to effectively deal with non-stationary switching opponents in a repeated game context. Our main contribution is a framework for online learning and planning against opponents that switch strategies. We present how two opponent modelling techniques work within the framework and prove the usefulness of the approach experimentally in the iterated prisoner's dilemma, when the opponent is modelled as an agent that switches between different strategies (e.g. TFT, Pavlov and Bully). The results of both models were compared against each other and against a state-of-the-art non-stationary reinforcement learning technique. Results reflect that our approach obtains competitive results without needing an offline training phase, as opposed to the state-of-the-art techniques.

Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning.

Science.gov (United States)

Zhu, Lusha; Mathewson, Kyle E; Hsu, Ming

2012-01-31

Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents' beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs.

Translating visual information into action predictions: Statistical learning in action and nonaction contexts.

Science.gov (United States)

Monroy, Claire D; Gerson, Sarah A; Hunnius, Sabine

2018-05-01

Humans are sensitive to the statistical regularities in action sequences carried out by others. In the present eyetracking study, we investigated whether this sensitivity can support the prediction of upcoming actions when observing unfamiliar action sequences. In two between-subjects conditions, we examined whether observers would be more sensitive to statistical regularities in sequences performed by a human agent versus self-propelled 'ghost' events. Secondly, we investigated whether regularities are learned better when they are associated with contingent effects. Both implicit and explicit measures of learning were compared between agent and ghost conditions. Implicit learning was measured via predictive eye movements to upcoming actions or events, and explicit learning was measured via both uninstructed reproduction of the action sequences and verbal reports of the regularities. The findings revealed that participants, regardless of condition, readily learned the regularities and made correct predictive eye movements to upcoming events during online observation. However, different patterns of explicit-learning outcomes emerged following observation: Participants were most likely to re-create the sequence regularities and to verbally report them when they had observed an actor create a contingent effect. These results suggest that the shift from implicit predictions to explicit knowledge of what has been learned is facilitated when observers perceive another agent's actions and when these actions cause effects. These findings are discussed with respect to the potential role of the motor system in modulating how statistical regularities are learned and used to modify behavior.

Innovative agents in cancer prevention.

Science.gov (United States)

Manson, Margaret M; Farmer, Peter B; Gescher, Andreas; Steward, William P

2005-01-01

efficacy, to learn how to use them effectively in combination, and in some cases to redesign them to improve potency or bioavailability. These ideas are illustrated by dietary agents such as indole-3-carbinol (I3C), epigallocatechin gallate (EGCG), curcumin and resveratrol, all of which appear to have a number of different molecular targets, impinging on several signalling pathways. Ultimately it may be possible not only to suppress tumours and to extend quality of life by administering appropriate diet-derived molecules, but also to refine the definition of a cancer chemopreventive diet.

"Notice of Violation of IEEE Publication Principles" Multiobjective Reinforcement Learning: A Comprehensive Overview.

Science.gov (United States)

Liu, Chunming; Xu, Xin; Hu, Dewen

2013-04-29

Reinforcement learning is a powerful mechanism for enabling agents to learn in an unknown environment, and most reinforcement learning algorithms aim to maximize some numerical value, which represents only one long-term objective. However, multiple long-term objectives are exhibited in many real-world decision and control problems; therefore, recently, there has been growing interest in solving multiobjective reinforcement learning (MORL) problems with multiple conflicting objectives. The aim of this paper is to present a comprehensive overview of MORL. In this paper, the basic architecture, research topics, and naive solutions of MORL are introduced at first. Then, several representative MORL approaches and some important directions of recent research are reviewed. The relationships between MORL and other related research are also discussed, which include multiobjective optimization, hierarchical reinforcement learning, and multi-agent reinforcement learning. Finally, research challenges and open problems of MORL techniques are highlighted.

[Alkylating agents].

Science.gov (United States)

Pourquier, Philippe

2011-11-01

With the approval of mechlorethamine by the FDA in 1949 for the treatment of hematologic malignancies, alkylating agents are the oldest class of anticancer agents. Even though their clinical use is far beyond the use of new targeted therapies, they still occupy a major place in specific indications and sometimes represent the unique option for the treatment of refractory diseases. Here, we are reviewing the major classes of alkylating agents and their mechanism of action, with a particular emphasis for the new generations of alkylating agents. As for most of the chemotherapeutic agents used in the clinic, these compounds are derived from natural sources. With a complex but original mechanism of action, they represent new interesting alternatives for the clinicians, especially for tumors that are resistant to conventional DNA damaging agents. We also briefly describe the different strategies that have been or are currently developed to potentiate the use of classical alkylating agents, especially the inhibition of pathways that are involved in the repair of DNA lesions induced by these agents. In this line, the development of PARP inhibitors is a striking example of the recent regain of interest towards the "old" alkylating agents.

Combining multi agent paradigm and memetic computing for personalized and adaptive learning experiences

NARCIS (Netherlands)

Acampora, G.; Gaeta, M.; Loia, V.

2011-01-01

Learning is a critical support mechanism for industrial and academic organizations to enhance the skills of employees and students and, consequently, the overall competitiveness in the new economy. The remarkable velocity and volatility of modern knowledge require novel learning methods offering

Emerging Paradigms in Machine Learning

CERN Document Server

Jain, Lakhmi; Howlett, Robert

2013-01-01

This  book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The  multidisciplinary nature of machine learning makes it a very fascinating and popular area for research.  The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems.  Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary ...

Accelerating Multiagent Reinforcement Learning by Equilibrium Transfer.

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Hu, Yujing; Gao, Yang; An, Bo

2015-07-01

An important approach in multiagent reinforcement learning (MARL) is equilibrium-based MARL, which adopts equilibrium solution concepts in game theory and requires agents to play equilibrium strategies at each state. However, most existing equilibrium-based MARL algorithms cannot scale due to a large number of computationally expensive equilibrium computations (e.g., computing Nash equilibria is PPAD-hard) during learning. For the first time, this paper finds that during the learning process of equilibrium-based MARL, the one-shot games corresponding to each state's successive visits often have the same or similar equilibria (for some states more than 90% of games corresponding to successive visits have similar equilibria). Inspired by this observation, this paper proposes to use equilibrium transfer to accelerate equilibrium-based MARL. The key idea of equilibrium transfer is to reuse previously computed equilibria when each agent has a small incentive to deviate. By introducing transfer loss and transfer condition, a novel framework called equilibrium transfer-based MARL is proposed. We prove that although equilibrium transfer brings transfer loss, equilibrium-based MARL algorithms can still converge to an equilibrium policy under certain assumptions. Experimental results in widely used benchmarks (e.g., grid world game, soccer game, and wall game) show that the proposed framework: 1) not only significantly accelerates equilibrium-based MARL (up to 96.7% reduction in learning time), but also achieves higher average rewards than algorithms without equilibrium transfer and 2) scales significantly better than algorithms without equilibrium transfer when the state/action space grows and the number of agents increases.

Curiosity driven reinforcement learning for motion planning on humanoids

Science.gov (United States)

Frank, Mikhail; Leitner, Jürgen; Stollenga, Marijn; Förster, Alexander; Schmidhuber, Jürgen

2014-01-01

Most previous work on artificial curiosity (AC) and intrinsic motivation focuses on basic concepts and theory. Experimental results are generally limited to toy scenarios, such as navigation in a simulated maze, or control of a simple mechanical system with one or two degrees of freedom. To study AC in a more realistic setting, we embody a curious agent in the complex iCub humanoid robot. Our novel reinforcement learning (RL) framework consists of a state-of-the-art, low-level, reactive control layer, which controls the iCub while respecting constraints, and a high-level curious agent, which explores the iCub's state-action space through information gain maximization, learning a world model from experience, controlling the actual iCub hardware in real-time. To the best of our knowledge, this is the first ever embodied, curious agent for real-time motion planning on a humanoid. We demonstrate that it can learn compact Markov models to represent large regions of the iCub's configuration space, and that the iCub explores intelligently, showing interest in its physical constraints as well as in objects it finds in its environment. PMID:24432001

eTeacher: Providing Personalized Assistance to E-Learning Students

Science.gov (United States)

Schiaffino, Silvia; Garcia, Patricio; Amandi, Analia

2008-01-01

In this paper we present eTeacher, an intelligent agent that provides personalized assistance to e-learning students. eTeacher observes a student's behavior while he/she is taking online courses and automatically builds the student's profile. This profile comprises the student's learning style and information about the student's performance, such…

Enhancing Transportation Education through On-line Simulation using an Agent-Based Demand and Assignment Model

OpenAIRE

Shanjiang Zhu; Feng Xie; David Levinson

2005-01-01

This research explores the effectiveness of using simulation as a tool for enhancing classroom learning in the Civil Engineering Department of the University of Minnesota at Twin Cities. The authors developed a modern transportation planning software package, Agent-based Demand and Assignment Model (ADAM), that is consistent with our present understanding of travel behavior, that is platform independent, and that is easy to learn and is thus usable by students. An in-class project incorporate...

Animated pedagogical agents: do they advance student motivation and learning in an inquiry learning environment?

NARCIS (Netherlands)

van der Meij, Hans; van der Meij, Jan; Harmsen, Ruth

2012-01-01

Student behavior in inquiry learning environments has often been found to be in need of (meta)cognitive support. Two pilots revealed that students might also benefit from motivational support in such an environment. An experiment with 61 junior high school students (ages 14-16) compared three

Animated Pedagogical Agents: Do they advance student motivation and learning in an inquiry learning environment?

NARCIS (Netherlands)

van der Meij, Hans; van der Meij, Jan; Harmsen, R.

2012-01-01

Student behavior in inquiry learning environments has often been found to be in need of (meta)cognitive support. Two pilots revealed that students might also benefit from motivational support in such an environment. An experiment with 61 junior high school students (ages 14-16) compared three

Can young children learn words from a robot?

OpenAIRE

Moriguchi, Yusuke; Kanda, Takayuki; Ishiguro, Hiroshi; Shimada, Yoko; Itakura, Shoji

2011-01-01

Young children generally learn words from other people. Recent research has shown that children can learn new actions and skills from nonhuman agents. This study examines whether young children could learn words from a robot. Preschool children were shown a video in which either a woman (human condition) or a mechanical robot (robot condition) labeled novel objects. Then the children were asked to select the objects according to the names used in the video. The results revealed that children ...

MOOCs as Change Agents to Boost Innovation in Higher Education Learning Arenas

Science.gov (United States)

Ossiannilsson, Ebba; Altinay, Fahriye; Altinay, Zehra

2016-01-01

Massive open online courses (MOOCs) provide opportunities for learners to benefit from initiatives that are promoted by prestigious universities worldwide. The introduction of MOOCs in 2008 has since then transformed education globally. Consequently, MOOCs should be acknowledged as a pedagogical innovation and recognized as change agents and…

Incorporating BDI Agents into Human-Agent Decision Making Research

Science.gov (United States)

Kamphorst, Bart; van Wissen, Arlette; Dignum, Virginia

Artificial agents, people, institutes and societies all have the ability to make decisions. Decision making as a research area therefore involves a broad spectrum of sciences, ranging from Artificial Intelligence to economics to psychology. The Colored Trails (CT) framework is designed to aid researchers in all fields in examining decision making processes. It is developed both to study interaction between multiple actors (humans or software agents) in a dynamic environment, and to study and model the decision making of these actors. However, agents in the current implementation of CT lack the explanatory power to help understand the reasoning processes involved in decision making. The BDI paradigm that has been proposed in the agent research area to describe rational agents, enables the specification of agents that reason in abstract concepts such as beliefs, goals, plans and events. In this paper, we present CTAPL: an extension to CT that allows BDI software agents that are written in the practical agent programming language 2APL to reason about and interact with a CT environment.

Chemical warfare agents.

Science.gov (United States)

Kuca, Kamil; Pohanka, Miroslav

2010-01-01

Chemical warfare agents are compounds of different chemical structures. Simple molecules such as chlorine as well as complex structures such as ricin belong to this group. Nerve agents, vesicants, incapacitating agents, blood agents, lung-damaging agents, riot-control agents and several toxins are among chemical warfare agents. Although the use of these compounds is strictly prohibited, the possible misuse by terrorist groups is a reality nowadays. Owing to this fact, knowledge of the basic properties of these substances is of a high importance. This chapter briefly introduces the separate groups of chemical warfare agents together with their members and the potential therapy that should be applied in case someone is intoxicated by these agents.

Logics for Intelligent Agents and Multi-Agent Systems

NARCIS (Netherlands)

Meyer, John-Jules Charles

2014-01-01

This chapter presents the history of the application of logic in a quite popular paradigm in contemporary computer science and artificial intelligence, viz. the area of intelligent agents and multi-agent systems. In particular we discuss the logics that have been used to specify single agents, the

Sticking with the nice guy: trait warmth information impairs learning and modulates person perception brain network activity.

Science.gov (United States)

Lee, Victoria K; Harris, Lasana T

2014-12-01

Social learning requires inferring social information about another person, as well as evaluating outcomes. Previous research shows that prior social information biases decision making and reduces reliance on striatal activity during learning (Delgado, Frank, & Phelps, Nature Neuroscience 8 (11): 1611-1618, 2005). A rich literature in social psychology on person perception demonstrates that people spontaneously infer social information when viewing another person (Fiske & Taylor, 2013) and engage a network of brain regions, including the medial prefrontal cortex, temporal parietal junction, superior temporal sulcus, and precuneus (Amodio & Frith, Nature Reviews Neuroscience, 7(4), 268-277, 2006; Haxby, Gobbini, & Montgomery, 2004; van Overwalle Human Brain Mapping, 30, 829-858, 2009). We investigate the role of these brain regions during social learning about well-established dimensions of person perception-trait warmth and trait competence. We test the hypothesis that activity in person perception brain regions interacts with learning structures during social learning. Participants play an investment game where they must choose an agent to invest on their behalf. This choice is guided by cues signaling trait warmth or trait competence based on framing of monetary returns. Trait warmth information impairs learning about human but not computer agents, while trait competence information produces similar learning rates for human and computer agents. We see increased activation to warmth information about human agents in person perception brain regions. Interestingly, activity in person perception brain regions during the decision phase negatively predicts activity in the striatum during feedback for trait competence inferences about humans. These results suggest that social learning may engage additional processing within person perception brain regions that hampers learning in economic contexts.

What Can Reinforcement Learning Teach Us About Non-Equilibrium Quantum Dynamics

Science.gov (United States)

Bukov, Marin; Day, Alexandre; Sels, Dries; Weinberg, Phillip; Polkovnikov, Anatoli; Mehta, Pankaj

Equilibrium thermodynamics and statistical physics are the building blocks of modern science and technology. Yet, our understanding of thermodynamic processes away from equilibrium is largely missing. In this talk, I will reveal the potential of what artificial intelligence can teach us about the complex behaviour of non-equilibrium systems. Specifically, I will discuss the problem of finding optimal drive protocols to prepare a desired target state in quantum mechanical systems by applying ideas from Reinforcement Learning [one can think of Reinforcement Learning as the study of how an agent (e.g. a robot) can learn and perfect a given policy through interactions with an environment.]. The driving protocols learnt by our agent suggest that the non-equilibrium world features possibilities easily defying intuition based on equilibrium physics.

Biological Agents

Science.gov (United States)

... E-Tools Safety and Health Topics / Biological Agents Biological Agents This page requires that javascript be enabled ... 202) 693-2300 if additional assistance is required. Biological Agents Menu Overview In Focus: Ebola Frederick A. ...

Adaptive, Distributed Control of Constrained Multi-Agent Systems

Science.gov (United States)

Bieniawski, Stefan; Wolpert, David H.

2004-01-01

Product Distribution (PO) theory was recently developed as a broad framework for analyzing and optimizing distributed systems. Here we demonstrate its use for adaptive distributed control of Multi-Agent Systems (MASS), i.e., for distributed stochastic optimization using MAS s. First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (Probability dist&&on on the joint state of the agents. When the game in question is a team game with constraints, that equilibrium optimizes the expected value of the team game utility, subject to those constraints. One common way to find that equilibrium is to have each agent run a Reinforcement Learning (E) algorithm. PD theory reveals this to be a particular type of search algorithm for minimizing the Lagrangian. Typically that algorithm i s quite inefficient. A more principled alternative is to use a variant of Newton's method to minimize the Lagrangian. Here we compare this alternative to RL-based search in three sets of computer experiments. These are the N Queen s problem and bin-packing problem from the optimization literature, and the Bar problem from the distributed RL literature. Our results confirm that the PD-theory-based approach outperforms the RL-based scheme in all three domains.

Which is the best intrinsic motivation signal for learning multiple skills?

Directory of Open Access Journals (Sweden)

Vieri Giuliano Santucci

2013-11-01

Full Text Available Humans and other biological agents are able to autonomously learn and cache different skills in the absence of any biological pressure or any assigned task. In this respect, Intrinsic Motivations (i.e. motivations not connected to reward-related stimuli play a cardinal role in animal learning, and can be considered as a fundamental tool for developing more autonomous and more adaptive artificial agents. In this work, we provide an exhaustive analysis of a scarcely investigated problem: which kind of IM reinforcement signal is the most suitable for driving the acquisition of multiple skills in the shortest time? To this purpose we implemented an artificial agent with a hierarchical architecture that allows to learn and cache different skills. We tested the system in a setup with continuous states and actions, in particular, with a cinematic robotic arm that has to learn different reaching tasks. We compare the results of different versions of the system driven by several different intrinsic motivation signals. The results show a that intrinsic reinforcements purely based on the knowledge of the system are not appropriate to guide the acquisition of multiple skills, and b that the stronger the link between the IM signal and the competence of the system, the better the performance.

Using Hierarchical Machine Learning to Improve Player Satisfaction in a Soccer Videogame

OpenAIRE

Collins, Brian; Rovatsos, Michael

2006-01-01

This paper describes an approach to using a hierarchical machine learning model in a two player 3D physics-based soccer video game to improve human player satisfaction. Learning is accomplished at two layers to form a complete game-playing agent such that higher level strategy learning is dependent on lower-level learning of basic behaviors.Supervised learning is used to train neural networks on human data to model the basic behaviors. The reinforcement learning algorithms Sarsa (λ) and Q(λ) ...

Dynamically analyzing cell interactions in biological environments using multiagent social learning framework.

Science.gov (United States)

Zhang, Chengwei; Li, Xiaohong; Li, Shuxin; Feng, Zhiyong

2017-09-20

Biological environment is uncertain and its dynamic is similar to the multiagent environment, thus the research results of the multiagent system area can provide valuable insights to the understanding of biology and are of great significance for the study of biology. Learning in a multiagent environment is highly dynamic since the environment is not stationary anymore and each agent's behavior changes adaptively in response to other coexisting learners, and vice versa. The dynamics becomes more unpredictable when we move from fixed-agent interaction environments to multiagent social learning framework. Analytical understanding of the underlying dynamics is important and challenging. In this work, we present a social learning framework with homogeneous learners (e.g., Policy Hill Climbing (PHC) learners), and model the behavior of players in the social learning framework as a hybrid dynamical system. By analyzing the dynamical system, we obtain some conditions about convergence or non-convergence. We experimentally verify the predictive power of our model using a number of representative games. Experimental results confirm the theoretical analysis. Under multiagent social learning framework, we modeled the behavior of agent in biologic environment, and theoretically analyzed the dynamics of the model. We present some sufficient conditions about convergence or non-convergence and prove them theoretically. It can be used to predict the convergence of the system.

A New Architecture for Making Moral Agents Based on C4.5 Decision Tree Algorithm

OpenAIRE

Meisam Azad-Manjiri

2014-01-01

Regarding to the influence of robots in the various fields of life, the issue of trusting to them is important, especially when a robot deals with people directly. One of the possible ways to get this confidence is adding a moral dimension to the robots. Therefore, we present a new architecture in order to build moral agents that learn from demonstrations. This agent is based on Beauchamp and Childress’s principles of biomedical ethics (a type of deontological theory) and uses decision tree a...

Performative Intra-Action of a Paper Plane and a Child: Exploring Scientific Concepts as Agentic Playmates

Science.gov (United States)

Haus, Jana Maria

2018-05-01

This work uses new materialist perspectives (Barad 2007; Lenz Taguchi 2014; Rautio in Children's Geographies, 11(4), 394-408, 2013) to examine an exploration of concepts as agents and the question how intra-action of human and non-human bodies lead to the investigation of scientific concepts, relying on an article by de Freitas and Palmer (Cultural Studies of Science Education, 11(4), 1201-1222, 2016). Through an analysis of video stills of a case study, a focus on classroom assemblages shows how the intra-actions of human and non-human bodies (one 5-year-old boy, a piece of paper that becomes a paper plane and the concepts of force and flight) lead to an intertwining and intersecting of play, learning, and becoming. Video recordings were used to qualitatively analyze three questions, which emerged through and resulted from the intra-action of researcher and data. This paper aims at addressing a prevalent gap in the research literature on science learning from a materialist view. Findings of the analysis show that human and non-human bodies together become through and for another to jointly and agentically intra-act in exploring and learning about science. Implications for learning and teaching science are that teachers could attempt to focus on setting up the learning environment differently, so that children have time and access to materials that matter to them and that, as "Hultman (2011) claims […] `whisper, answer, demand and offer'" (Areljung forthcoming, p. 77) themselves to children in the learning and teaching environment.

Steps toward Learning Mechanics Using Fan Cart Video Demonstrations

Science.gov (United States)

Lattery, Mark

2011-01-01

The Newtonian force concept is very difficult for introductory students to learn. One obstacle to learning is a premature focus on gravity-driven motions, such as vertical free fall, rolling motion on an inclined plane, and the Atwood's machine. In each case, the main agent of motion ("gravity") cannot be seen, heard, or controlled by the student.…

A Collective Case Study of Secondary Students' Model-Based Inquiry on Natural Selection through Programming in an Agent-Based Modeling Environment

Science.gov (United States)

Xiang, Lin

2011-01-01

This is a collective case study seeking to develop detailed descriptions of how programming an agent-based simulation influences a group of 8th grade students' model-based inquiry (MBI) by examining students' agent-based programmable modeling (ABPM) processes and the learning outcomes. The context of the present study was a biology unit on…

The Impact of Robot Tutor Nonverbal Social Behavior on Child Learning

Directory of Open Access Journals (Sweden)

James Kennedy

2017-04-01

Full Text Available Several studies have indicated that interacting with social robots in educational contexts may lead to a greater learning than interactions with computers or virtual agents. As such, an increasing amount of social human–robot interaction research is being conducted in the learning domain, particularly with children. However, it is unclear precisely what social behavior a robot should employ in such interactions. Inspiration can be taken from human–human studies; this often leads to an assumption that the more social behavior an agent utilizes, the better the learning outcome will be. We apply a nonverbal behavior metric to a series of studies in which children are taught how to identify prime numbers by a robot with various behavioral manipulations. We find a trend, which generally agrees with the pedagogy literature, but also that overt nonverbal behavior does not account for all learning differences. We discuss the impact of novelty, child expectations, and responses to social cues to further the understanding of the relationship between robot social behavior and learning. We suggest that the combination of nonverbal behavior and social cue congruency is necessary to facilitate learning.

Usability evaluation of an eLearning course presenting a regional destination: the case of “Ticino Switzerland Travel Specialist”

OpenAIRE

Kalbaska, Nadzeya; Jovic, Angelina; Cantoni, Lorenzo

2013-01-01

In recent years Destination Management Organizations (DMOs) have started to offer eLearning courses, which train travel agents and tour operators to promote better tourism destinations. eLearning courses increase marketing activities of a DMO also they meet the needs of travel agents, who are in search of new unique selling points in the threatening context of eTourism disintermediation. Nevertheless, the user-friendly appearance of these eLearning courses and their easiness of use are stil...

Contrast agents for cardiac angiography: effects of a nonionic agent vs. a standard ionic agent

International Nuclear Information System (INIS)

Bettmann, M.A.; Bourdillon, P.D.; Barry, W.H.; Brush, K.A.; Levin, D.C.

1984-01-01

The effects on cardiac hemodynamics and of a standard contrast agent, sodium methylglucamine diatrizoate [Renografin 76] were compared with the effects of a new nonionic agent (iohexol) in a double-blind study in 51 patietns undergoing coronary angiography and left ventriculography. No significant alteration in measured blood parameters occurred with either contrast agent. Hemodynamic changes occurred with both, but were significantly greater with the standard renografin than with the low-osmolality, nonionic iohexol. After left ventriculography, heart rate increased and peripheral arterial pressure fell with both agents, but less with iohexol. It is concluded that iohexol causes less alteration in cardiac function than does the agent currently most widely used. Nonionic contrast material is likely to improve the safety of coronary angiography, particularly in those patients at greatest risk

The influence of active vision on the exoskeleton of intelligent agents

Science.gov (United States)

Smith, Patrice; Terry, Theodore B.

2016-04-01

Chameleonization occurs when a self-learning autonomous mobile system's (SLAMR) active vision scans the surface of which it is perched causing the exoskeleton to changes colors exhibiting a chameleon effect. Intelligent agents having the ability to adapt to their environment and exhibit key survivability characteristics of its environments would largely be due in part to the use of active vision. Active vision would allow the intelligent agent to scan its environment and adapt as needed in order to avoid detection. The SLAMR system would have an exoskeleton, which would change, based on the surface it was perched on; this is known as the "chameleon effect." Not in the common sense of the term, but from the techno-bio inspired meaning as addressed in our previous paper. Active vision, utilizing stereoscopic color sensing functionality would enable the intelligent agent to scan an object within its close proximity, determine the color scheme, and match it; allowing the agent to blend with its environment. Through the use of its' optical capabilities, the SLAMR system would be able to further determine its position, taking into account spatial and temporal correlation and spatial frequency content of neighboring structures further ensuring successful background blending. The complex visual tasks of identifying objects, using edge detection, image filtering, and feature extraction are essential for an intelligent agent to gain additional knowledge about its environmental surroundings.

Identifying Learning Behaviors by Contextualizing Differential Sequence Mining with Action Features and Performance Evolution

Science.gov (United States)

Kinnebrew, John S.; Biswas, Gautam

2012-01-01

Our learning-by-teaching environment, Betty's Brain, captures a wealth of data on students' learning interactions as they teach a virtual agent. This paper extends an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs sequence mining techniques to…

DYNAMIC AND INCREMENTAL EXPLORATION STRATEGY IN FUSION ADAPTIVE RESONANCE THEORY FOR ONLINE REINFORCEMENT LEARNING

Directory of Open Access Journals (Sweden)

Budhitama Subagdja

2016-06-01

Full Text Available One of the fundamental challenges in reinforcement learning is to setup a proper balance between exploration and exploitation to obtain the maximum cummulative reward in the long run. Most protocols for exploration bound the overall values to a convergent level of performance. If new knowledge is inserted or the environment is suddenly changed, the issue becomes more intricate as the exploration must compromise the pre-existing knowledge. This paper presents a type of multi-channel adaptive resonance theory (ART neural network model called fusion ART which serves as a fuzzy approximator for reinforcement learning with inherent features that can regulate the exploration strategy. This intrinsic regulation is driven by the condition of the knowledge learnt so far by the agent. The model offers a stable but incremental reinforcement learning that can involve prior rules as bootstrap knowledge for guiding the agent to select the right action. Experiments in obstacle avoidance and navigation tasks demonstrate that in the configuration of learning wherein the agent learns from scratch, the inherent exploration model in fusion ART model is comparable to the basic E-greedy policy. On the other hand, the model is demonstrated to deal with prior knowledge and strike a balance between exploration and exploitation.

Brahmi rasayana Improves Learning and Memory in Mice

Directory of Open Access Journals (Sweden)

Hanumanthachar Joshi

2006-01-01

Full Text Available Cure of cognitive disorders such as amnesia, attention deficit and Alzheimer's disease is still a nightmare in the field of medicine. Nootropic agents such as piracetam, aniracetam and choline esterase inhibitors like Donepezil® are being used to improve memory, mood and behavior, but the resulting side effects associated with these agents have made their use limited. The present study was undertaken to assess the potential of Brahmi rasayana (BR as a memory enhancer. BR (100 and 200 mg kg−1 p.o. was administered for eight successive days to both young and aged mice. Elevated plus maze and passive-avoidance paradigm were employed to evaluate learning and memory parameters. Scopolamine (0.4 mg kg−1 i.p. was used to induce amnesia in mice. The effect of BR on whole brain AChE activity was also assessed. Piracetam (200 mg kg−1 i.p. was used as a standard nootropic agent. BR significantly improved learning and memory in young mice and reversed the amnesia induced by both scopolamine (0.4 mg kg−1 i.p. and natural aging. BR significantly decreased whole brain acetyl cholinesterase activity. BR might prove to be a useful memory restorative agent in the treatment of dementia seen in elderly.

Exploring complex dynamics in multi agent-based intelligent systems: Theoretical and experimental approaches using the Multi Agent-based Behavioral Economic Landscape (MABEL) model

Science.gov (United States)

Alexandridis, Konstantinos T.

This dissertation adopts a holistic and detailed approach to modeling spatially explicit agent-based artificial intelligent systems, using the Multi Agent-based Behavioral Economic Landscape (MABEL) model. The research questions that addresses stem from the need to understand and analyze the real-world patterns and dynamics of land use change from a coupled human-environmental systems perspective. Describes the systemic, mathematical, statistical, socio-economic and spatial dynamics of the MABEL modeling framework, and provides a wide array of cross-disciplinary modeling applications within the research, decision-making and policy domains. Establishes the symbolic properties of the MABEL model as a Markov decision process, analyzes the decision-theoretic utility and optimization attributes of agents towards comprising statistically and spatially optimal policies and actions, and explores the probabilogic character of the agents' decision-making and inference mechanisms via the use of Bayesian belief and decision networks. Develops and describes a Monte Carlo methodology for experimental replications of agent's decisions regarding complex spatial parcel acquisition and learning. Recognizes the gap on spatially-explicit accuracy assessment techniques for complex spatial models, and proposes an ensemble of statistical tools designed to address this problem. Advanced information assessment techniques such as the Receiver-Operator Characteristic curve, the impurity entropy and Gini functions, and the Bayesian classification functions are proposed. The theoretical foundation for modular Bayesian inference in spatially-explicit multi-agent artificial intelligent systems, and the ensembles of cognitive and scenario assessment modular tools build for the MABEL model are provided. Emphasizes the modularity and robustness as valuable qualitative modeling attributes, and examines the role of robust intelligent modeling as a tool for improving policy-decisions related to land

Reinforcement Learning State-of-the-Art

CERN Document Server

Wiering, Marco

2012-01-01

Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade. The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together the...

Making predictions in a changing world-inference, uncertainty, and learning.

Science.gov (United States)

O'Reilly, Jill X

2013-01-01

To function effectively, brains need to make predictions about their environment based on past experience, i.e., they need to learn about their environment. The algorithms by which learning occurs are of interest to neuroscientists, both in their own right (because they exist in the brain) and as a tool to model participants' incomplete knowledge of task parameters and hence, to better understand their behavior. This review focusses on a particular challenge for learning algorithms-how to match the rate at which they learn to the rate of change in the environment, so that they use as much observed data as possible whilst disregarding irrelevant, old observations. To do this algorithms must evaluate whether the environment is changing. We discuss the concepts of likelihood, priors and transition functions, and how these relate to change detection. We review expected and estimation uncertainty, and how these relate to change detection and learning rate. Finally, we consider the neural correlates of uncertainty and learning. We argue that the neural correlates of uncertainty bear a resemblance to neural systems that are active when agents actively explore their environments, suggesting that the mechanisms by which the rate of learning is set may be subject to top down control (in circumstances when agents actively seek new information) as well as bottom up control (by observations that imply change in the environment).

Efficient abstraction selection in reinforcement learning

NARCIS (Netherlands)

Seijen, H. van; Whiteson, S.; Kester, L.

2013-01-01

This paper introduces a novel approach for abstraction selection in reinforcement learning problems modelled as factored Markov decision processes (MDPs), for which a state is described via a set of state components. In abstraction selection, an agent must choose an abstraction from a set of

Model-Based Knowing: How Do Students Ground Their Understanding About Climate Systems in Agent-Based Computer Models?

Science.gov (United States)

Markauskaite, Lina; Kelly, Nick; Jacobson, Michael J.

2017-12-01

This paper gives a grounded cognition account of model-based learning of complex scientific knowledge related to socio-scientific issues, such as climate change. It draws on the results from a study of high school students learning about the carbon cycle through computational agent-based models and investigates two questions: First, how do students ground their understanding about the phenomenon when they learn and solve problems with computer models? Second, what are common sources of mistakes in students' reasoning with computer models? Results show that students ground their understanding in computer models in five ways: direct observation, straight abstraction, generalisation, conceptualisation, and extension. Students also incorporate into their reasoning their knowledge and experiences that extend beyond phenomena represented in the models, such as attitudes about unsustainable carbon emission rates, human agency, external events, and the nature of computational models. The most common difficulties of the students relate to seeing the modelled scientific phenomenon and connecting results from the observations with other experiences and understandings about the phenomenon in the outside world. An important contribution of this study is the constructed coding scheme for establishing different ways of grounding, which helps to understand some challenges that students encounter when they learn about complex phenomena with agent-based computer models.

Cognitive Support Embedded in Self-Regulated E-Learning Systems for Students with Special Learning Needs

Science.gov (United States)

Chatzara, K.; Karagiannidis, C.; Stamatis, D.

2016-01-01

This paper presents an anthropocentric approach in human-machine interaction in the area of self-regulated e-learning. In an attempt to enhance communication mediated through computers for pedagogical use we propose the incorporation of an intelligent emotional agent that is represented by a synthetic character with multimedia capabilities,…

We Care about You: Incorporating Pet Characteristics with Educational Agents through Reciprocal Caring Approach

Science.gov (United States)

Chen, Zhi-Hong

2012-01-01

Although different educational agents have been proposed to facilitate student learning, most of them operate from a "smart" (i.e., intelligent and autonomous) perspective. Recently, a so-called "non-smart" perspective is also attracting increasing interest, and is now regarded as a topic worthwhile of researching. To this end,…

A Composite Agent Architecture for Multi-Agent Simulations

OpenAIRE

VanPutte, Michael; Osborn, Brian; Hiles, John

2002-01-01

CGF Computer Generated Forces and Behavioral Representation The MOVES Institute’s Computer-Generated Autonomy Group has focused on a research goal of modeling complex and adaptive behavior while at the same time making the behavior easier to create and control. This research has led to several techniques for agent construction, that includes a social and organization relationship management engine, a composite agent architecture, an agent goal apparatus, a structure for capturi...

Increasing participation of people with learning disabilities in bowel screening.

Science.gov (United States)

Gray, Jonathan

2018-03-08

Learning disability nurses have a key role in addressing the health inequalities experienced by people with learning disabilities. People with learning disabilities are less likely to participate in bowel screening than other sectors of the population, despite there being evidence of this population being at an increased risk of developing bowel cancer. There are a range of barriers at individual and systemic levels that impact on participation in bowel screening by people with learning disabilities. Actions to address these barriers have been identified in the literature and learning disability nurses are a key agent of change in enabling people with learning disabilities to participate in the national screening programmes.

TACtic- A Multi Behavioral Agent for Trading Agent Competition

Science.gov (United States)

Khosravi, Hassan; Shiri, Mohammad E.; Khosravi, Hamid; Iranmanesh, Ehsan; Davoodi, Alireza

Software agents are increasingly being used to represent humans in online auctions. Such agents have the advantages of being able to systematically monitor a wide variety of auctions and then make rapid decisions about what bids to place in what auctions. They can do this continuously and repetitively without losing concentration. To provide a means of evaluating and comparing (benchmarking) research methods in this area the trading agent competition (TAC) was established. This paper describes the design, of TACtic. Our agent uses multi behavioral techniques at the heart of its decision making to make bidding decisions in the face of uncertainty, to make predictions about the likely outcomes of auctions, and to alter the agent's bidding strategy in response to the prevailing market conditions.

Multiagent Cooperative Learning Strategies for Pursuit-Evasion Games

Directory of Open Access Journals (Sweden)

Jong Yih Kuo

2015-01-01

Full Text Available This study examines the pursuit-evasion problem for coordinating multiple robotic pursuers to locate and track a nonadversarial mobile evader in a dynamic environment. Two kinds of pursuit strategies are proposed, one for agents that cooperate with each other and the other for agents that operate independently. This work further employs the probabilistic theory to analyze the uncertain state information about the pursuers and the evaders and uses case-based reasoning to equip agents with memories and learning abilities. According to the concepts of assimilation and accommodation, both positive-angle and bevel-angle strategies are developed to assist agents in adapting to their environment effectively. The case study analysis uses the Recursive Porous Agent Simulation Toolkit (REPAST to implement a multiagent system and demonstrates superior performance of the proposed approaches to the pursuit-evasion game.

Lifelong Learning and Healthy Ageing : The Significance of Music as an Agent of Change

NARCIS (Netherlands)

Smilde, Rineke; Bisschop Boele, Evert

2016-01-01

This chapter gives an overview on the Healthy Ageing research portfolio of the research group Lifelong Learning in Music (Hanze University of Applied Sciences Groningen, the Netherlands). Lifelong learning enables musicians to respond to the continuously changing context in which they are working

'Proactive' use of cue-context congruence for building reinforcement learning's reward function.

Science.gov (United States)

Zsuga, Judit; Biro, Klara; Tajti, Gabor; Szilasi, Magdolna Emma; Papp, Csaba; Juhasz, Bela; Gesztelyi, Rudolf

2016-10-28

Reinforcement learning is a fundamental form of learning that may be formalized using the Bellman equation. Accordingly an agent determines the state value as the sum of immediate reward and of the discounted value of future states. Thus the value of state is determined by agent related attributes (action set, policy, discount factor) and the agent's knowledge of the environment embodied by the reward function and hidden environmental factors given by the transition probability. The central objective of reinforcement learning is to solve these two functions outside the agent's control either using, or not using a model. In the present paper, using the proactive model of reinforcement learning we offer insight on how the brain creates simplified representations of the environment, and how these representations are organized to support the identification of relevant stimuli and action. Furthermore, we identify neurobiological correlates of our model by suggesting that the reward and policy functions, attributes of the Bellman equitation, are built by the orbitofrontal cortex (OFC) and the anterior cingulate cortex (ACC), respectively. Based on this we propose that the OFC assesses cue-context congruence to activate the most context frame. Furthermore given the bidirectional neuroanatomical link between the OFC and model-free structures, we suggest that model-based input is incorporated into the reward prediction error (RPE) signal, and conversely RPE signal may be used to update the reward-related information of context frames and the policy underlying action selection in the OFC and ACC, respectively. Furthermore clinical implications for cognitive behavioral interventions are discussed.

El agente de cambio en el desarrollo de las organizaciones

Directory of Open Access Journals (Sweden)

Joaquín GAIRÍN SALLÁN

2009-08-01

Full Text Available RESUMEN: Los directivos desempeñan un papel crucial en el éxito de las reformas e innovaciones cuando actúan como agentes de cambio. La presente aportación caracteriza el agente de cambio, reparando en sus principales cualidades, capacidades y actitudes; también, destaca la importancia de una intervención que prime la visión global y estratégica frente a intervenciones parciales, gestione y lidere a la vez y asuma la necesidad permanente de aprender de la práctica. Las actuaciones de los agentes de cambio no acostumbran a desempeñarse en un escenario de facilidades y, a menudo, se encuentran con determinadas problemáticas y resistencias que dificultan la implantación de las mejoras institucionales pretendidas. En cualquier caso, avanzar en el desarrollo de organizaciones educativas exitosas exige otorgar a los agentes de cambio un cierto liderazgo capaz de contribuir al desarrollo de las mismas.ABSTRACT: Directors play a crucial role in the success of reforms and innovations when they act as agents of change. This contribution characterises agents of change, examining their main qualities, capacities and attitudes; it also highlights the importance of an intervention that rewards a global and strategic vision, as opposed to biased interventions, that manages and leads at the same time and that assumes the permanent need to learn from experience. The actions of agents of change do not tend to take place in easy scenarios, and often encounter certain problems and resistances that make it hard to implant the institutional improvements being sought. In whatever case, advancing the development of successful educational organisations requires granting agents of change a certain leadership that is able to contribute to the development of the same. Finally, the confluence between curricular development, organisational development and professional development in efficiency and improvement processes at education centres reminds us of the

An Agent-Based Simulation for Investigating the Impact of Stereotypes on Task-Oriented Group Formation

Science.gov (United States)

Maghami, Mahsa; Sukthankar, Gita

In this paper, we introduce an agent-based simulation for investigating the impact of social factors on the formation and evolution of task-oriented groups. Task-oriented groups are created explicitly to perform a task, and all members derive benefits from task completion. However, even in cases when all group members act in a way that is locally optimal for task completion, social forces that have mild effects on choice of associates can have a measurable impact on task completion performance. In this paper, we show how our simulation can be used to model the impact of stereotypes on group formation. In our simulation, stereotypes are based on observable features, learned from prior experience, and only affect an agent's link formation preferences. Even without assuming stereotypes affect the agents' willingness or ability to complete tasks, the long-term modifications that stereotypes have on the agents' social network impair the agents' ability to form groups with sufficient diversity of skills, as compared to agents who form links randomly. An interesting finding is that this effect holds even in cases where stereotype preference and skill existence are completely uncorrelated.

[Hypoxia and memory. Specific features of nootropic agents effects and their use].

Science.gov (United States)

Voronina, T A

2000-01-01

Hypoxia and hypoxic adaptation are powerful factors of controlling memory and behavior processes. Acute hypoxia exerts a differential impact on different deficits of mnestic and cognitive functions. Instrumental reflexes of active and passive avoidance, negative learning, behavior with a change in the stereotype of learning are more greatly damaged. Memory with spatial and visual differentiation and their rearrangement change to a lesser extent and conditional reflexes are not deranged. In this contract, altitude hypoxic adaptation enhances information fixation and increases the degree and duration of retention of temporary relations. Nootropic agents with an antihypoxic action exert a marked effect on hypoxia-induced cognitive and memory disorders and the magnitude of this effect depends on the ration of proper nootropic to antihypoxic components in the spectrum of the drugs' pharmacological activity. The agents that combine a prevailing antiamnestic effect and a marked and moderate antihypoxic action (mexidole, nooglutil, pyracetam, beglymin, etc.) are most effective in eliminating different hypoxia-induced cognitive and memory disorders, nootropic drugs that have a pronounced antiamnestic activity (centrophenoxine, etc.) and no antihypoxic component also restore the main types of mnestic disorders after hypoxia, but to a lesser extent.

Modeling and simulation of virtual human's coordination based on multi-agent systems

Science.gov (United States)

Zhang, Mei; Wen, Jing-Hua; Zhang, Zu-Xuan; Zhang, Jian-Qing

2006-10-01

The difficulties and hotspots researched in current virtual geographic environment (VGE) are sharing space and multiusers operation, distributed coordination and group decision-making. The theories and technologies of MAS provide a brand-new environment for analysis, design and realization of distributed opening system. This paper takes cooperation among virtual human in VGE which multi-user participate in as main researched object. First we describe theory foundation truss of VGE, and present the formalization description of Multi-Agent System (MAS). Then we detailed analyze and research arithmetic of collectivity operating behavior learning of virtual human based on best held Genetic Algorithm(GA), and establish dynamics action model which Multi-Agents and object interact dynamically and colony movement strategy. Finally we design a example which shows how 3 evolutional Agents cooperate to complete the task of colony pushing column box, and design a virtual world prototype of virtual human pushing box collectively based on V-Realm Builder 2.0, moreover we make modeling and dynamic simulation with Simulink 6.

Concurrent Unimodal Learning Enhances Multisensory Responses of Bi-Directional Crossmodal Learning in Robotic Audio-Visual Tracking

DEFF Research Database (Denmark)

Shaikh, Danish; Bodenhagen, Leon; Manoonpong, Poramate

2018-01-01

modalities to independently update modality-specific neural weights on a moment-by-moment basis, in response to dynamic changes in noisy sensory stimuli. The circuit is embodied as a non-holonomic robotic agent that must orient a towards a moving audio-visual target. The circuit continuously learns the best...

A reinforcement learning model of joy, distress, hope and fear

Science.gov (United States)

Broekens, Joost; Jacobs, Elmer; Jonker, Catholijn M.

2015-07-01

In this paper we computationally study the relation between adaptive behaviour and emotion. Using the reinforcement learning framework, we propose that learned state utility, ?, models fear (negative) and hope (positive) based on the fact that both signals are about anticipation of loss or gain. Further, we propose that joy/distress is a signal similar to the error signal. We present agent-based simulation experiments that show that this model replicates psychological and behavioural dynamics of emotion. This work distinguishes itself by assessing the dynamics of emotion in an adaptive agent framework - coupling it to the literature on habituation, development, extinction and hope theory. Our results support the idea that the function of emotion is to provide a complex feedback signal for an organism to adapt its behaviour. Our work is relevant for understanding the relation between emotion and adaptation in animals, as well as for human-robot interaction, in particular how emotional signals can be used to communicate between adaptive agents and humans.

Multi-Agent Pathfinding with n Agents on Graphs with n Vertices

DEFF Research Database (Denmark)

Förster, Klaus-Tycho; Groner, Linus; Hoefler, Torsten

2017-01-01

We investigate the multi-agent pathfinding (MAPF) problem with $n$ agents on graphs with $n$ vertices: Each agent has a unique start and goal vertex, with the objective of moving all agents in parallel movements to their goal s.t.~each vertex and each edge may only be used by one agent at a time....... We give a combinatorial classification of all graphs where this problem is solvable in general, including cases where the solvability depends on the initial agent placement. Furthermore, we present an algorithm solving the MAPF problem in our setting, requiring O(n²) rounds, or O(n³) moves...... of individual agents. Complementing these results, we show that there are graphs where Omega(n²) rounds and Omega(n³) moves are required for any algorithm....

A Teachable Agent Game Engaging Primary School Children to Learn Arithmetic Concepts and Reasoning

Science.gov (United States)

Pareto, Lena

2014-01-01

In this paper we will describe a learning environment designed to foster conceptual understanding and reasoning in mathematics among younger school children. The learning environment consists of 48 2-player game variants based on a graphical model of arithmetic where the mathematical content is intrinsically interwoven with the game idea. The…

10th International Conference on Practical Applications of Agents and Multi-Agent Systems

CERN Document Server

Pérez, Javier; Golinska, Paulina; Giroux, Sylvain; Corchuelo, Rafael; Trends in Practical Applications of Agents and Multiagent Systems

2012-01-01

PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an evolution of the International Workshop on Practical Applications of Agents and Multi-Agent Systems. PAAMS is an international yearly tribune to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development of Agents and Multi-Agent Systems.   This volume presents the papers that have been accepted for the 2012 in the workshops: Workshop on Agents for Ambient Assisted Living, Workshop on Agent-Based Solutions for Manufacturing and Supply Chain and Workshop on Agents and Multi-agent systems for Enterprise Integration.

Riot Control Agents

Science.gov (United States)

... Submit What's this? Submit Button Facts About Riot Control Agents Interim document Recommend on Facebook Tweet Share Compartir What riot control agents are Riot control agents (sometimes referred to ...

Collaboration model in e-learning for universities based on agents

OpenAIRE

Bernuy, Augusto E.; García, Víctor M.

2006-01-01

The paper presents the basic requirements that must cover distance education processes (“e-learning”) in universities. We show the concepts of instruction design, an adaptive learning model for evaluating necessities, accreditation, and quality proposal. The experience indicates that to obtain good results we should evaluate the differences between the criteria of the professor and the criteria of the student about: the educative aspects, the user reaction (in each perspective), the reading a...

Understanding Collective Learning and Human Agency in Diverse ...

African Journals Online (AJOL)

2018-05-07

May 7, 2018 ... new social systems that are more sustainable and socially just. ... collection to these international deliberations about the role of education in enabling ... learning can foster and contribute to the development of change agents ...

Experimenting with Different Bulking Agents in an Aerobic Food Waste Composter

Science.gov (United States)

Chann, S.

2016-12-01

With one third of Hong Kong's solid wastage being food scraps, reducing food waste has become crucial. The ISF Academy, a Hong Kong private school, had an A900 Rocket Food Composter installed in 2013, hoping to reduce its carbon footprint. The 27 metric tons of food wastage produced annually by the school is put through an aerobic process and the wastage is converted into humus. The composter has a capacity of 1750 litres of food and it produces humus every 14 days. The base of the humus consists of a bulking agent and food waste (2:1). A bulking agent is a carbon based material used to absorb moisture and odors, add structure and air and eliminate bugs from humus. This study contains comparative data on a few of the listed bulking agents: Hemp, Kenaf, rapeseed oil straw, miscanthus and shredded cardboard. The aim of this study is to determine an alternative reliable, affordable and suitable bulking agent to wood shavings: the current agent used. The humus produced must pass regulations for "general agricultural use" as it is used for experiential learning and gardening with primary school students. Over 500 children are participating in the school's plantation project, producing legumes for the school cafeteria. ISF pioneers and sets an example for other Hong Kong schools, showing that a composting and plantation scheme, not only proves to have environmental benefits but also educational uses.

An intelligent service-based layered architecture for e learning and assessment

International Nuclear Information System (INIS)

Javaid, Q.; Arif, F.

2017-01-01

The rapid advancement in ICT (Information and Communication Technology) is causing a paradigm shift in eLearning domain. Traditional eLearning systems suffer from certain shortcomings like tight coupling of system components, lack of personalization, flexibility, and scalability and performance issues. This study aims at addressing these challenges through an MAS (Multi Agent System) based multi-layer architecture supported by web services. The foremost objective of this study is to enhance learning process efficiency by provision of flexibility features for learning and assessment processes. Proposed architecture consists of two sub-system namely eLearning and eAssesssment. This architecture comprises of five distinct layers for each sub-system, with active agents responsible for miscellaneous tasks including content handling, updating, resource optimization, load handling and provision of customized environments for learners and instructors. Our proposed architecture aims at establishment of a facilitation level to learners as well as instructors for convenient acquisition and dissemination of knowledge. Personalization features like customized environments, personalized content retrieval and recommendations, adaptive assessment and reduced response time, are believed to significantly enhance learning and tutoring experience. In essence characteristics like intelligence, personalization, interactivity, usability, laidback accessibility and security, signify aptness of proposed architecture for improving conventional learning and assessment processes. Finally we have evaluated our proposed architecture by means of analytical comparison and survey considering certain quality attributes. (author)

Acquisition of a space representation by a naive agent from sensorimotor invariance and proprioceptive compensation

Directory of Open Access Journals (Sweden)

Gurvan Le Clec’H

2016-11-01

Full Text Available In this article, we present a simple agent which learns an internal representation of space without a priori knowledge of its environment, body, or sensors. The learned environment is seen as an internal space representation. This representation is isomorphic to the group of transformations applied to the environment. The model solves certain theoretical and practical issues encountered in previous work in sensorimotor contingency theory. Considering the mathematical description of the internal representation, analysis of its properties and simulations, we prove that this internal representation is equivalent to knowledge of space.

Constructing Secure Mobile Agent Systems Using the Agent Operating System

NARCIS (Netherlands)

van t Noordende, G.J.; Overeinder, B.J.; Timmer, R.J.; Brazier, F.M.; Tanenbaum, A.S.

2009-01-01

Designing a secure and reliable mobile agent system is a difficult task. The agent operating system (AOS) is a building block that simplifies this task. AOS provides common primitives required by most mobile agent middleware systems, such as primitives for secure communication, secure and

Nondestructive Intervention to Multi-Agent Systems through an Intelligent Agent

Science.gov (United States)

Han, Jing; Wang, Lin

2013-01-01

For a given multi-agent system where the local interaction rule of the existing agents can not be re-designed, one way to intervene the collective behavior of the system is to add one or a few special agents into the group which are still treated as normal agents by the existing ones. We study how to lead a Vicsek-like flocking model to reach synchronization by adding special agents. A popular method is to add some simple leaders (fixed-headings agents). However, we add one intelligent agent, called ‘shill’, which uses online feedback information of the group to decide the shill's moving direction at each step. A novel strategy for the shill to coordinate the group is proposed. It is strictly proved that a shill with this strategy and a limited speed can synchronize every agent in the group. The computer simulations show the effectiveness of this strategy in different scenarios, including different group sizes, shill speed, and with or without noise. Compared to the method of adding some fixed-heading leaders, our method can guarantee synchronization for any initial configuration in the deterministic scenario and improve the synchronization level significantly in low density groups, or model with noise. This suggests the advantage and power of feedback information in intervention of collective behavior. PMID:23658695

Nondestructive intervention to multi-agent systems through an intelligent agent.

Directory of Open Access Journals (Sweden)

Jing Han

Full Text Available For a given multi-agent system where the local interaction rule of the existing agents can not be re-designed, one way to intervene the collective behavior of the system is to add one or a few special agents into the group which are still treated as normal agents by the existing ones. We study how to lead a Vicsek-like flocking model to reach synchronization by adding special agents. A popular method is to add some simple leaders (fixed-headings agents. However, we add one intelligent agent, called 'shill', which uses online feedback information of the group to decide the shill's moving direction at each step. A novel strategy for the shill to coordinate the group is proposed. It is strictly proved that a shill with this strategy and a limited speed can synchronize every agent in the group. The computer simulations show the effectiveness of this strategy in different scenarios, including different group sizes, shill speed, and with or without noise. Compared to the method of adding some fixed-heading leaders, our method can guarantee synchronization for any initial configuration in the deterministic scenario and improve the synchronization level significantly in low density groups, or model with noise. This suggests the advantage and power of feedback information in intervention of collective behavior.

The active learning educational organisation: a case study of innovation in electrical engineering education

NARCIS (Netherlands)

Vos, Henk

2004-01-01

The introduction of active learning in engineering education is often started by enthusiastic teachers or change agents. They usually encounter resistance from stakeholders such as colleagues, department boards or students. For a successful introduction these stakeholders all have to learn what

Social Facilitation Effects by Pedagogical Conversational Agent: Lexical Network Analysis in an Online Explanation Task

Science.gov (United States)

Hayashi, Yugo

2015-01-01

The present study investigates web-based learning activities of undergraduate students who generate explanations about a key concept taught in a large-scale classroom. The present study used an online system with Pedagogical Conversational Agent (PCA), asked to explain about the key concept from different points and provided suggestions and…

Reexposure to the Amnestic Agent Alleviates Cycloheximide-Induced Retrograde Amnesia for Reactivated and Extinction Memories

Science.gov (United States)

Briggs, James F.; Olson, Brian P.

2013-01-01

We investigated whether reexposure to an amnestic agent would reverse amnesia for extinction of learned fear similar to that of a reactivated memory. When cycloheximide (CHX) was administered immediately after a brief cue-induced memory reactivation (15 sec) and an extended extinction session (12 min) rats showed retrograde amnesia for both…

Improving diagnostic accuracy using agent-based distributed data mining system.

Science.gov (United States)

Sridhar, S

2013-09-01

The use of data mining techniques to improve the diagnostic system accuracy is investigated in this paper. The data mining algorithms aim to discover patterns and extract useful knowledge from facts recorded in databases. Generally, the expert systems are constructed for automating diagnostic procedures. The learning component uses the data mining algorithms to extract the expert system rules from the database automatically. Learning algorithms can assist the clinicians in extracting knowledge automatically. As the number and variety of data sources is dramatically increasing, another way to acquire knowledge from databases is to apply various data mining algorithms that extract knowledge from data. As data sets are inherently distributed, the distributed system uses agents to transport the trained classifiers and uses meta learning to combine the knowledge. Commonsense reasoning is also used in association with distributed data mining to obtain better results. Combining human expert knowledge and data mining knowledge improves the performance of the diagnostic system. This work suggests a framework of combining the human knowledge and knowledge gained by better data mining algorithms on a renal and gallstone data set.

Organizations as Socially Constructed Agents in the Agent Oriented Paradigm

NARCIS (Netherlands)

G. Boella (Guido); L.W.N. van der Torre (Leon)

2005-01-01

htmlabstractIn this paper we propose a new role for the agent metaphor in the definition of the organizational structure of multiagent systems. The agent metaphor is extended to consider as agents also social entities like organizations, groups and normative systems, so that mental attitudes can

Improved Student Learning through a Faculty Learning Community: How Faculty Collaboration Transformed a Large-Enrollment Course from Lecture to Student Centered

Science.gov (United States)

Elliott, Emily R.; Reason, Robert D.; Coffman, Clark R.; Gangloff, Eric J.; Raker, Jeffrey R.; Powell-Coffman, Jo Anne; Ogilvie, Craig A.

2016-01-01

Undergraduate introductory biology courses are changing based on our growing understanding of how students learn and rapid scientific advancement in the biological sciences. At Iowa State University, faculty instructors are transforming a second-semester large-enrollment introductory biology course to include active learning within the lecture setting. To support this change, we set up a faculty learning community (FLC) in which instructors develop new pedagogies, adapt active-learning strategies to large courses, discuss challenges and progress, critique and revise classroom interventions, and share materials. We present data on how the collaborative work of the FLC led to increased implementation of active-learning strategies and a concurrent improvement in student learning. Interestingly, student learning gains correlate with the percentage of classroom time spent in active-learning modes. Furthermore, student attitudes toward learning biology are weakly positively correlated with these learning gains. At our institution, the FLC framework serves as an agent of iterative emergent change, resulting in the creation of a more student-centered course that better supports learning. PMID:27252298

Neuroprotective "agents" in surgery. Secret "agent" man, or common "agent" machine?

Science.gov (United States)

Andrews, R. J.

1999-01-01

The search for clinically-effective neuroprotective agents has received enormous support in recent years--an estimated $200 million by pharmaceutical companies on clinical trials for traumatic brain injury alone. At the same time, the pathophysiology of brain injury has proved increasingly complex, rendering the likelihood of a single agent "magic bullet" even more remote. On the other hand, great progress continues with technology that makes surgery less invasive and less risky. One example is the application of endovascular techniques to treat coronary artery stenosis, where both the invasiveness of sternotomy and the significant neurological complication rate (due to microemboli showering the cerebral vasculature) can be eliminated. In this paper we review aspects of intraoperative neuroprotection both present and future. Explanations for the slow progress on pharmacologic neuroprotection during surgery are presented. Examples of technical advances that have had great impact on neuroprotection during surgery are given both from coronary artery stenosis surgery and from surgery for Parkinson's disease. To date, the progress in neuroprotection resulting from such technical advances is an order of magnitude greater than that resulting from pharmacologic agents used during surgery. The progress over the last 20 years in guidance during surgery (CT and MRI image-guidance) and in surgical access (endoscopic and endovascular techniques) will soon be complemented by advances in our ability to evaluate biological tissue intraoperatively in real-time. As an example of such technology, the NASA Smart Probe project is considered. In the long run (i.e., in 10 years or more), pharmacologic "agents" aimed at the complex pathophysiology of nervous system injury in man will be the key to true intraoperative neuroprotection. In the near term, however, it is more likely that mundane "agents" based on computers, microsensors, and microeffectors will be the major impetus to improved

A Neural Network Model to Learn Multiple Tasks under Dynamic Environments

Science.gov (United States)

Tsumori, Kenji; Ozawa, Seiichi

When environments are dynamically changed for agents, the knowledge acquired in an environment might be useless in future. In such dynamic environments, agents should be able to not only acquire new knowledge but also modify old knowledge in learning. However, modifying all knowledge acquired before is not efficient because the knowledge once acquired may be useful again when similar environment reappears and some knowledge can be shared among different environments. To learn efficiently in such environments, we propose a neural network model that consists of the following modules: resource allocating network, long-term & short-term memory, and environment change detector. We evaluate the model under a class of dynamic environments where multiple function approximation tasks are sequentially given. The experimental results demonstrate that the proposed model possesses stable incremental learning, accurate environmental change detection, proper association and recall of old knowledge, and efficient knowledge transfer.

Advances on Practical Applications of Agents and Multi-Agent Systems 10th International Conference on Practical Applications of Agents and Multi-Agent Systems

CERN Document Server

Müller, Jörg; Rodríguez, Juan; Pérez, Javier

2012-01-01

Research on Agents and Multi-Agent Systems has matured during the last decade and many effective applications of this technology are now deployed. PAAMS provides an international forum to present and discuss the latest scientific developments and their effective applications, to assess the impact of the approach, and to facilitate technology transfer. PAAMS started as a local initiative, but has since grown to become THE international yearly platform to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development and deployment of Agents and Multi-Agent Systems. PAAMS intends to bring together researchers and developers from industry and the academic world to report on the latest scientific and technical advances on the application of multi-agent systems, to discuss and debate the major ...

Highlights on Practical Applications of Agents and Multi-Agent Systems 10th International Conference on Practical Applications of Agents and Multi-Agent Systems

CERN Document Server

Sánchez, Miguel; Mathieu, Philippe; Rodríguez, Juan; Adam, Emmanuel; Ortega, Alfonso; Moreno, María; Navarro, Elena; Hirsch, Benjamin; Lopes-Cardoso, Henrique; Julián, Vicente

2012-01-01

Research on Agents and Multi-Agent Systems has matured during the last decade and many effective applications of this technology are now deployed. PAAMS provides an international forum to present and discuss the latest scientific developments and their effective applications, to assess the impact of the approach, and to facilitate technology transfer. PAAMS started as a local initiative, but has since grown to become THE international yearly platform to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development and deployment of Agents and Multi-Agent Systems. PAAMS intends to bring together researchers and developers from industry and the academic world to report on the latest scientific and technical advances on the application of multi-agent systems, to discuss and debate the major ...

Radiographic scanning agent

International Nuclear Information System (INIS)

Bevan, J.A.

1983-01-01

This invention relates to radiodiagnostic agents and more particularly to a composition and method for preparing a highly effective technetium-99m-based bone scanning agent. One deficiency of x-ray examination is the inability of that technique to detect skeletal metastases in their incipient stages. It has been discovered that the methanehydroxydiphosphonate bone mineral-seeking agent is unique in that it provides the dual benefits of sharp radiographic imaging and excellent lesion detection when used with technetium-99m. This agent can also be used with technetium-99m for detecting soft tissue calcification in the manner of the inorganic phosphate radiodiagnostic agents

The significance of 'facilitator as a change agent'--organisational learning culture in aged care home settings.

Science.gov (United States)

Grealish, Laurie; Henderson, Amanda; Quero, Fritz; Phillips, Roslyn; Surawski, May

2015-04-01

To explore the impact of an educational programme focused on social behaviours and relationships on organisational learning culture in the residential aged care context. The number of aged care homes will continue to rise as the frail older elderly live longer, requiring more formal care and support. As with other small- to medium-sized health services, aged care homes are faced with the challenge of continuous development of the workforce and depend upon registered nurses to lead staff development. A mixed-method evaluation research design was used to determine the impact of an educational programme focused on social aspects of learning on organisational learning culture. One hundred and fifty-nine (pre) and 143 (post) participants from three aged care homes completed the Clinical Learning Organisational Culture survey, and three participant-researcher registered nurse clinical educators provided regular journal entries for review. While each site received the same educational programme over a six-month period, the change in organisational learning culture at each site was notably different. Two aged care homes had significant improvements in affiliation, one in accomplishment and one in recognition. The educators' journals differed in the types of learning observed and interventions undertaken, with Eucalyptus focused on organisational change, Grevillea focused on group (student) change and the Wattle focused on individual or situational change. Clinical educator activities appear to have a significant effect on organisational learning culture, with a focus on the organisational level having the greatest positive effect on learning culture and on individual or situational level having a limited effect. Clinical educator facilitation that is focused on organisational rather than individual interests may offer a key to improving organisational learning culture. © 2014 John Wiley & Sons Ltd.

Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents

OpenAIRE

Keizer, Simon; Guhe, Markus; Cuayáhuitl, Heriberto; Efstathiou, Ioannis; Engelbrecht, Klaus-Peter; Dobre, Mihai; Lascarides, Alexandra; Lemon, Oliver

2017-01-01

In this paper we present a comparative evaluation of various negotiation strategies within an online version of the game “Settlers of Catan”. The comparison is based on human subjects playing games against artificial game-playing agents (‘bots’) which implement different negotiation dialogue strategies, using a chat dialogue interface to negotiate trades. Our results suggest that a negotiation strategy that uses persuasion, as well as a strategy that is trained from data using Deep Reinforcem...

Distributed scheduling for autonomous vehicles by reinforcement learning; Kyoka gakushu ni yoru mujin hansosha no bunsangata scheduling

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