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

Enric Plaza, Santiago Ontañón (auth.), Eduardo Alonso, Daniel Kudenko, Dimitar Kazakov (eds.)3540400680, 9783540400684

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

Table of contents :
Cooperative Multiagent Learning….Pages 1-17
Reinforcement Learning Approaches to Coordination in Cooperative Multi-agent Systems….Pages 18-32
Cooperative Learning Using Advice Exchange….Pages 33-48
Environmental Risk, Cooperation, and Communication Complexity….Pages 49-65
Multiagent Learning for Open Systems: A Study in Opponent Classification….Pages 66-87
Situated Cognition and the Role of Multi-agent Models in Explaining Language Structure….Pages 88-109
Adapting Populations of Agents….Pages 110-124
The Evolution of Communication Systems by Adaptive Agents….Pages 125-140
An Agent Architecture to Design Self-Organizing Collectives: Principles and Application….Pages 141-158
Evolving Preferences among Emergent Groups of Agents….Pages 159-173
Structuring Agents for Adaptation….Pages 174-186
Stochastic Simulation of Inherited Kinship-Driven Altruism….Pages 187-201
Learning in Multiagent Systems: An Introduction from a Game-Theoretic Perspective….Pages 202-215
The Implications of Philosophical Foundations for Knowledge Representation and Learning in Agents….Pages 216-238
Using Cognition and Learning to Improve Agents’ Reactions….Pages 239-259
TTree: Tree-Based State Generalization with Temporally Abstract Actions….Pages 260-290
Using Landscape Theory to Measure Learning Difficulty for Adaptive Agents….Pages 291-305
Relational Reinforcement Learning for Agents in Worlds with Objects….Pages 306-322