Learning and Adaption in Multi-Agent Systems

First International Workshop, LAMAS 2005, Utrecht, The Netherlands, July 25, 2005, Revised Selected Papers

  • Karl Tuyls
  • Pieter Jan’t Hoen
  • Katja Verbeeck
  • Sandip Sen

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3898)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 3898)

Table of contents

  1. Front Matter
  2. Pieter Jan ’t Hoen, Karl Tuyls, Liviu Panait, Sean Luke, J. A. La Poutré
    Pages 1-46
  3. Mazda Ahmadi, Peter Stone
    Pages 47-70
  4. Ann Nowé, Katja Verbeeck, Maarten Peeters
    Pages 71-85
  5. Stéphane Airiau, Sandip Sen
    Pages 86-99
  6. Bikramjit Banerjee, Jing Peng
    Pages 100-114
  7. Constança Oliveira e Sousa, Luis Custódio
    Pages 139-154
  8. Austin McDonald, Sandip Sen
    Pages 155-164
  9. Kagan Tumer, Adrian Agogino
    Pages 177-191
  10. Tom Croonenborghs, Karl Tuyls, Jan Ramon, Maurice Bruynooghe
    Pages 192-206
  11. Peter Vrancx, Ann Nowé, Kris Steenhaut
    Pages 207-215
  12. Back Matter

About these proceedings

Introduction

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, i.e., 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.

Keywords

Evolution adaptive agents agent communication agent coordination agent environments agent programming agent reasoning agents distributed artificial intelligence learning machine learning multi-agent learning systems multi-agent system reinforcement learning robot

Editors and affiliations

  • Karl Tuyls
    • 1
  • Pieter Jan’t Hoen
    • 2
  • Katja Verbeeck
    • 3
  • Sandip Sen
    • 4
  1. 1.MICC-IKATUniversiteit MaastrichtThe Netherlands
  2. 2.Center for Mathematics and Computer Science (CWI)AmsterdamThe Netherlands
  3. 3.KaHo Sint-Lieven, Information Technology GroupGentBelgium
  4. 4.Department of Mathematical and Computer ScienceUniversity of TulsaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/11691839
  • Copyright Information Springer-Verlag Berlin Heidelberg 2006
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-33053-0
  • Online ISBN 978-3-540-33059-2
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book