Adaptation and Learning in Multi-Agent Systems

IJCAI' 95 Workshop, Montreal, Canada, August 21, 1995. Proceedings.
Mitarbeit:Weiß, Gerhard; Sen, Sandip

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Lecture Notes in Computer Science 1042
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 978-3-540-60923-0
• 1996
• Seitenzahl: 256
• Erscheinungstermin: 27. Februar 1996
• Englisch
• Abmessung: 235mm x 155mm x 15mm
• Gewicht: 362g
• ISBN-13: 9783540609230
• ISBN-10: 3540609237
• Artikelnr.: 23170949

Inhaltsangabe

Adaptation and learning in multi-agent systems: Some remarks and a bibliography.- Refinement in agent groups.- Opponent modeling in multi-agent systems.- A multi-agent environment for department of defense distribution.- Mutually supervised learning in multiagent systems.- A framework for distributed reinforcement learning.- Evolving behavioral strategies in predators and prey.- To learn or not to learn .......- A user-adaptive interface agency for interaction with a virtual environment.- Learning in multi-robot systems.- Learn your opponent's strategy (in polynomial time)!.- Learning to reduce communication cost on task negotiation among multiple autonomous mobile robots.- On multiagent Q-learning in a semi-competitive domain.- Using reciprocity to adapt to others.- Multiagent coordination with learning classifier systems.