Learning and Adaption in Multi-Agent Systems First International Wor
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
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Subseries of Lecture Notes in Computer Science
3898
Karl Tuyls Pieter Jan ’t Hoen Katja Verbeeck Sandip Sen (Eds.)
Learning andAdaption in Multi-Agent Systems First International Workshop, LAMAS 2005 Utrecht, The Netherlands, July 25, 2005 Revised Selected Papers
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Series Editors Jaime G. Carbonell, Carnegie Mellon University, Pittsburgh, PA, USA Jörg Siekmann, University of Saarland, Saarbrücken, Germany Volume Editors Karl Tuyls Universiteit Maastricht Tongersestraat 6, Maastricht, The Netherlands E-mail: [email protected] Pieter Jan ’t Hoen Center for Mathematics and Computer Science (CWI) Kruislaan 413, P.O. Box 94079, 1090 GB Amsterdam, The Netherlands E-mail: [email protected] Katja Verbeeck Vrije Universiteit Brussel Faculty of Sciences (WE), Department of Computer Science Pleinlaan 2, 1050 Brussels, Belgium E-mail: [email protected] Sandip Sen University of Tulsa Department of Mathematical and Computer Sciences 600 S. College, Tulsa, OK 74104, USA E-mail: [email protected]
Library of Congress Control Number: 2006923098 CR Subject Classification (1998): I.2.11, I.2, C.2.4 LNCS Sublibrary: SL 7 – Artificial Intelligence ISSN ISBN-10 ISBN-13
0302-9743 3-540-33053-4 Springer Berlin Heidelberg New York 978-3-540-33053-0 Springer Berlin Heidelberg New York
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Preface This book contains selected and revised papers of the International Workshop on Learning 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 interactions. 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 autonomously. 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 st
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