Learning and Adaption in Multi-Agent Systems : First International Workshop, LAMAS 2005, Utrecht, The Netherlands, July 25, 2005, Revised Selected Papers / edited by Karl Tuyls, Pieter Jan 't Hoen, Katja Verbeeck, Sandip Sen.

Format:

Book

Contributor:

Tuyls, Karl, editor.

't Hoen, Pieter Jan, editor.

Verbeeck, Katja, editor.

Sen, Sandip, 1964- editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 3898.

Lecture Notes in Artificial Intelligence ; 3898

Language:

English

Subjects (All):

Artificial intelligence.

Computer networks.

Artificial Intelligence.

Computer Communication Networks.

Local Subjects:

Artificial Intelligence.

Computer Communication Networks.

Physical Description:

1 online resource (X, 217 pages).

Edition:

First edition 2006.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2006.

System Details:

text file PDF

Summary:

This book contains selected and revised papers of the International Workshop on Lea- ing and Adaptation in Multi-Agent Systems (LAMAS 2005), held at the AAMAS 2005 Conference in Utrecht, The Netherlands, July 26. An important aspect in multi-agent systems (MASs) is that the environment evolves over time, not only due to external environmental changes but also due to agent int- actions. For this reason it is important that an agent can learn, based on experience, and adapt its knowledge to make rational decisions and act in this changing environment autonomously. Machine learning techniques for single-agent frameworks are well established. Agents operate in uncertain environments and must be able to learn and act - tonomously. This task is, however, more complex when the agent interacts with other agents that have potentially different capabilities and goals. The single-agent case is structurally different from the multi-agent case due to the added dimension of dynamic interactions between the adaptive agents. Multi-agent learning, id est, the ability of the agents to learn how to cooperate and compete, becomes crucial in many domains. Autonomous agents and multi-agent systems (AAMAS) is an emerging multi-disciplinary area encompassing computer science, software engineering, biology, as well as cognitive and social sciences. A t- oretical framework, in which rationality of learning and interacting agents can be - derstood, is still under development in MASs, although there have been promising ?rst results.

Contents:

An Overview of Cooperative and Competitive Multiagent Learning

Multi-robot Learning for Continuous Area Sweeping

Learning Automata as a Basis for Multi Agent Reinforcement Learning

Learning Pareto-optimal Solutions in 2x2 Conflict Games

Unifying Convergence and No-Regret in Multiagent Learning

Implicit Coordination in a Network of Social Drivers: The Role of Information in a Commuting Scenario

Multiagent Traffic Management: Opportunities for Multiagent Learning

Dealing with Errors in a Cooperative Multi-agent Learning System

The Success and Failure of Tag-Mediated Evolution of Cooperation

An Adaptive Approach for the Exploration-Exploitation Dilemma and Its Application to Economic Systems

Efficient Reward Functions for Adaptive Multi-rover Systems

Multi-agent Relational Reinforcement Learning

Multi-type ACO for Light Path Protection.

Other Format:

Printed edition:

ISBN:

978-3-540-33059-2

9783540330592

Access Restriction:

Restricted for use by site license.