Adaptive Agents and Multi-Agent Systems: Adaptation and Multi-Agent Learning

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Eduardo Alonso, Daniel Kudenko, Dimitar Kazakov
Springer Science & Business Media, Apr 23, 2003 - Computers - 330 pages

Adaptive Agents and Multi-Agent Systems is an emerging and exciting interdisciplinary area of research and development involving artificial intelligence, computer science, software engineering, and developmental biology, as well as cognitive and social science.

This book surveys the state of the art in this emerging field by drawing together thoroughly selected reviewed papers from two related workshops; as well as papers by leading researchers specifically solicited for this book. The articles are organized into topical sections on

- learning, cooperation, and communication

- emergence and evolution in multi-agent systems

- theoretical foundations of adaptive agents

 

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Contents

Cooperative Multiagent Learning
1
Reinforcement Learning Approaches to Coordination in Cooperative Multiagent Systems
18
Cooperative Learning Using Advice Exchange
33
Environmental Risk Cooperation and Communication Complexity
49
A Study in Opponent Classification
66
Situated Cognition and the Role of Multiagent Models in Explaining Language Structure
88
Adapting Populations of Agents
110
The Evolution of Communication Systems by Adaptive Agents
125
Structuring Agents for Adaptation
174
Stochastic Simulation of Inherited KinshipDriven Altruism
187
An Introduction from a GameTheoretic Perspective
202
The Implications of Philosophical Foundations for Knowledge Representation and Learning in Agents
216
Using Cognition and Learning to Improve Agents Reactions
239
TreeBased State Generalization with Temporally Abstract Actions
260
Using Landscape Theory to Measure Learning Difficulty for Adaptive Agents
291
Relational Reinforcement Learning for Agents in Worlds with Objects
306

Principles and Application
141
Evolving Preferences among Emergent Groups of Agents
159

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