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Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments : Ecai'96 Workshop Ldais, Budapest, Hungary, August 13, 1996, Icmas'96 Workshop Liome, Kyoto, Japan, December 10, 1996 Selected Papers - Gerhard Weis

Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments

Ecai'96 Workshop Ldais, Budapest, Hungary, August 13, 1996, Icmas'96 Workshop Liome, Kyoto, Japan, December 10, 1996 Selected Papers

By: Gerhard Weis (Editor)

Paperback Published: 29th April 1997
ISBN: 9783540629344
Number Of Pages: 300

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The complexity of systems studied in distributed artificial intelligence (DAI), such as multi-agent systems, often makes it extremely difficult or even impossible to correctly and completely specify their behavioral repertoires and dynamics. There is broad agreement that such systems should be equipped with the ability to learn in order to improve their future performance autonomously. The interdisciplinary cooperation of researchers from DAI and machine learning (ML) has established a new and very active area of research and development enjoying steadily increasing attention from both communities. This state-of-the-art report documents current and ongoing developments in the area of learning in DAI systems. It is indispensable reading for anybody active in the area and will serve as a valuable source of information.

Reader's Guidep. 1
Challenges for Machine Learning in Cooperative Information Systemsp. 11
A Modular Approach to Multi-Agent Reinforcement Learningp. 25
Learning Real Team Solutionsp. 40
Learning by Linear Anticipation In Multi-Agent Systemsp. 62
Learning Coordinated Behavior in a Continuous Environmentp. 73
Multi-Agent Learning with the Success-Story Algorithmp. 82
On the Collaborative Object Search Team: A Formulationp. 94
Evolution of Coordination as a Metaphor for Learning in Multi-Agent Systemsp. 117
Correlating Internal Parameters and External Performance: Learning Soccer Agentsp. 137
Learning Agents' Reliability Through Bayesian Conditioning: A Simulation Experimentp. 151
A Study of Organizational Learning in Multi-Agent Systemsp. 168
Cooperative Case-Based Reasoningp. 180
Contract-Net-Based Learning in a User-Adaptive Interface Agencyp. 202
The Communication of Inductive Interferencesp. 223
Addressee Learning and Message Interception for Communication Load Reduction in Multiple Robot Environmentsp. 242
Learning and Communication in Multi-Agent Systemsp. 259
Investigating the Effects of Explicit Epistemology on a Distributed Learning Systemp. 276
Subject Indexp. 293
Table of Contents provided by Blackwell. All Rights Reserved.

ISBN: 9783540629344
ISBN-10: 3540629343
Series: Lecture Notes in Artificial Intelligence
Audience: General
Format: Paperback
Language: English
Number Of Pages: 300
Published: 29th April 1997
Publisher: Springer-Verlag Berlin and Heidelberg Gmbh & Co. Kg
Country of Publication: DE
Dimensions (cm): 23.39 x 15.6  x 1.65
Weight (kg): 0.44