Adaptation and Learning in Multi-Agent Systems
IJCAI' 95 Workshop, Montreal, Canada, August 21, 1995. Proceedings.
Published on 27. February 1996
Book
Paperback/Softback
XII, 568 pages
978-3-540-60923-0 (ISBN)
Description
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.
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.
More details
Series
Edition
1996 ed.
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
XII, 568 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 15 mm
Weight
394 gr
ISBN-13
978-3-540-60923-0 (9783540609230)
DOI
10.1007/3-540-60923-7
Schweitzer Classification
Content
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.