Book Announcement:


- Learning in multi-agent environments -

ed. by Gerhard Weiss

Lecture Notes in Artificial Intelligence, Volume 1221
ISBN 3-540-62934-3, 294 pp., softcover, price DM 68 / US$ 44.95
Available around May 10, 1997

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General Information

Book Structure

Table of Contents

Ordering Information

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General Information (from the Preface)

The intersection of distributed artificial intelligence and machine learning constitutes a relatively young but important area of research that has received steadily increasing attention in the past years. The reason for this attention is largely based on the insight that the complexity of the systems studied in distributed artificial intelligence often makes it extremely difficult or even impossible to correctly and completely specify their behavioral repertoires and their dynamics. It is therefore broadly agreed that these systems should be equipped with the ability to learn, that is, to improve their future performance on their own. This book documents current and ongoing developments in the area of learning in distributed artificial intelligence systems.

The book contains selected, revised, and extended versions of sixteen papers that were first presented at two related workshops held at the Twelfth European Conference on Artificial Intelligence (ECAI-96, Budapest, Hungary, August 11--16, 1996) and the Second International Conference on Multiagent Systems (ICMAS-96, Kyoto, Japan, December 9--13, 1996). These were the ECAI-96 workshop on ``Learning in Distributed Artificial Intelligence Systems'' (LDAIS) and the ICMAS-96 workshop on ``Learning, Interaction, and Organization in Multiagent Environments'' (LIOME). Additionally, the book contains the invited talk by Munindar Singh and Michael Huhns presented at the LDAIS workshop and an extensive Reader's Guide. The papers included in this book reflect the broad spectrum of learning in distributed artificial intelligence systems and the progress made in this area.

This book may be viewed as the successor of Weiss & Sen, eds., 1996, Adaption and Learning in Multi-Agent Systems, Lecture Notes in Artificial Intelligence Volume 1042, Springer-Verlag.

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Structure of the Book (from the Reader's Guide)

The book is structured as follows: (It is important to see that these parts are not orthogonal, but complement one another. For instance, agents may learn to cooperate by learning about each other's abilities, and in order to learn from one another the agents typically have to communicate.)

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Table of Contents

----- Introduction

"Reader's Guide", Gerhard Weiss, 1--10

"Challenges for Machine Learning in Cooperative Information Systems",
M. Singh and M. Huhns, 11--24

----- Part I: Learning, Cooperation and Competition

"A Modular Approach to Multi-Agent Reinforcement Learning"
N. Ono and K. Fukumoto, 25--39

" Learning Real Team Solutions",
C. Versino and L.M. Gambardella, 40--61

"Learning by Linear Anticipation in Multi-Agent Systems",
P. Davidsson, 62--72

"Learning Coordinated Behavior in a Continuous Environment",
N. Ono and Y. Fukuta, 73--81

"Multi-Agent Learning with the Success-Story Algorithm",
J. Schmidhuber and J. Zhao, 82--93

"On the Collaborative Object Search Team: A Formulation",
Y. Ye and J. Tsotsos, 94--116

"Evolution of Coordination as a Metaphor for Learning in
Multi-Agent Systems", A. Bazzan, 117--136

----- Part II: Learning About/From Other Agents and the World

"Correlating Internal Parameters and External Performance:
Learning Soccer Agents", R. Nadella and S. Sen, 137--150

"Learning Agents' Reliability Through Bayesian Conditioning:
A Simulation Experiment", A. Dragoni and Paolo Giorgini, 151--167

"A Study of Organizational Learning in Multi-Agent Systems",
M. Terabe, T. Washio, O. Katai, and T. Sawaragi, 168--179

"Cooperative Case-Based Reasoning",
E. Plaza, J. Arcos, and F. Martin, 180--201

"Contract-Net-Based Learning in a User-Adaptive Interface Agency",
B. Lenzmann and Ipke Wachsmuth, 202--222

----- Part III: Learning, Communication and Understanding

"The Communication of Inductive Inferences",
W. Davies and P. Edwards, 223--241

"Addressee Learning and Message Interception for Communication
Load Reduction in Multiple Robot Environments",
T. Ohko, K. Hiraki, and Y. Anzai, 242--258

"Learning and Communication in Multi-Agent Systems",
H. Friedrich, M. Kaiser, O. Rogalla, and R. Dillmann, 259--275

"Investigating the Effects of Explicit Epistemology on a Distributed
Learning System", N. Lacey, K. Nakata, and M. Lee, 276--292

----- Subject Index, 293--294

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Ordering Information

MEETS MACHINE LEARNING. Learning in Multi-Agent Environments",  
edited by Gerhard Weiss, and published by Springer-Verlag as 
Lecture Notes in Artificial Intelligence Volume 1221.  
ISBN 3-540-62934-3, 294 pp., softcover, price DM 68 / US$ 44.95.  
Available around May 10, 1997. 
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