Adaptation and Learning in Multi-Agent Systems

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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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.

• Adaptive Learning
• Adaptive Systeme
• Adaptive Systems
• Adaptives Lernen
• Agent-Based Technology
• Agentenbasierte Technologie
• Machine Learning
• Maschinelles Lernen
• Mehr-Agenten-Systeme
• Multi-Agent Systems
• reinforcement learning
• robot
• supervised learning

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