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πŸ“

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

✍ Scribed by Gerhard Weiß (auth.), Gerhard Weiß (eds.)


Publisher
Springer-Verlag Berlin Heidelberg
Year
1997
Tongue
English
Leaves
305
Series
Lecture Notes in Computer Science 1221 : Lecture Notes in Artificial Intelligence
Edition
1
Category
Library

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✦ Synopsis


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.

✦ Table of Contents


Reader's guide....Pages 1-10
Challenges for machine learning in cooperative information systems....Pages 11-24
A modular approach to multi-agent reinforcement learning....Pages 25-39
Learning real team solutions....Pages 40-61
Learning by linear anticipation in multi-agent systems....Pages 62-72
Learning coordinated behavior in a continuous environment....Pages 73-81
Multi-agent learning with the success-story algorithm....Pages 82-93
On the collaborative object search team: a formulation....Pages 94-116
Evolution of coordination as a metaphor for learning in multi-agent systems....Pages 117-136
Correlating internal parameters and external performance: Learning Soccer Agents....Pages 137-150
Learning agents' reliability through Bayesian Conditioning: A simulation experiment....Pages 151-167
A study of organizational learning in multiagents systems....Pages 168-179
Cooperative Case-based Reasoning....Pages 180-201
Contract-net-based learning in a user-adaptive interface agency....Pages 202-222
The communication of inductive inferences....Pages 223-241
Addressee Learning and Message Interception for communication load reduction in multiple robot environments....Pages 242-258
Learning and communication in Multi-Agent Systems....Pages 259-275
Investigating the effects of explicit epistemology on a Distributed learning system....Pages 276-292

✦ Subjects


Artificial Intelligence (incl. Robotics); Simulation and Modeling; Programming Languages, Compilers, Interpreters


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