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Adaptation and Learning in Multi-Agent Systems

IJCAI' 95 Workshop, Montreal, Canada, August 21, 1995. Proceedings.

  • Conference proceedings
  • © 1996

Overview

Editors:
  1. Gerhard Weiß
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  2. Sandip Sen
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 1042)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: IJCAI 1995.

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Table of contents (15 papers)

  1. Front Matter

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  2. Adaptation and learning in multi-agent systems: Some remarks and a bibliography

    • Gerhard Weiß
    Pages 1-21

  3. Refinement in agent groups

    • Ciara Byrne, Peter Edwards
    Pages 22-39

  4. Opponent modeling in multi-agent systems

    • David Carmel, Shaul Markovitch
    Pages 40-52

  5. A multi-agent environment for department of defense distribution

    • Laurence Glicoes, Richard Staats, Michael Huhns
    Pages 53-84

  6. Mutually supervised learning in multiagent systems

    • Claudia V. Goldman, Jeffrey S. Rosenschein
    Pages 85-96

  7. A framework for distributed reinforcement learning

    • Pan Gu, Anthony B. Maddox
    Pages 97-112

  8. Evolving behavioral strategies in predators and prey

    • Thomas Haynes, Sandip Sen
    Pages 113-126

  9. To learn or not to learn ......

    • Anupam Joshi
    Pages 127-139

  10. A user-adaptive interface agency for interaction with a virtual environment

    • Britta Lenzmann, Ipke Wachsmuth
    Pages 140-151

  11. Learning in multi-robot systems

    • Maja J. Matarić
    Pages 152-163

  12. Learn your opponent's strategy (in polynomial time)!

    • Yishay Mor, Claudia V. Goldman, Jeffrey S. Rosenschein
    Pages 164-176

  13. Learning to reduce communication cost on task negotiation among multiple autonomous mobile robots

    • Takuya Ohko, Kazuo Hiraki, Yuichiro Anzai
    Pages 177-190

  14. On multiagent Q-learning in a semi-competitive domain

    • Tuomas W. Sandholm, Robert H. Crites
    Pages 191-205

  15. Using reciprocity to adapt to others

    • Sandip Sen, Mahendra Sekaran
    Pages 206-217

  16. Multiagent coordination with learning classifier systems

    • Sandip Sen, Mahendra Sekaran
    Pages 218-233

  17. Back Matter

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Other volumes

  1. Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing

  2. Adaption and Learning in Multi-Agent Systems

Keywords

About this book

This book is based on the workshop on Adaptation and Learning in Multi-Agent Systems, held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI'95, in Montreal, Canada in August 1995.
The 14 thoroughly reviewed revised papers reflect the whole scope of current aspects in the field: they describe and analyze, both experimentally and theoretically, new learning and adaption approaches for situations in which several agents have to cooperate or compete. Also included, and aimed at the novice reader, are a comprehensive introductory survey on the area with 154 references listed and a subject index. As the first book solely devoted to this area, this volume documents the state of the art and is thus indispensable for anyone active or interested in the field.

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