Adaptive Agents and Multi-Agents Systems

Adaptation and Multi-Agent Learning

Eduardo Alonso, Daniel Kudenko, Dimitar Kazakov

322 pages, parution le 12/06/2003

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Résumé

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

Learning, Co-operation, and Communication
- Cooperative Multiagent Learning
- Reinforcement Learning Approaches to Coordination in
- Cooperative Multi-agent
- Cooperative Learning Using Advice Exchange
- Environmental Risk, Cooperation, and Communication Complexity
- Multiagent Learning for Open Systems: A Study in Opponent
- Classification
- Situated Cognition and the Role of Multi-agent Models
- in Explaining Language Structure
Emergence and Evolution in Multi-agent Systems
- Adapting Populations of Agents
- The Evolution of Communication Systems by Adaptive Agents
- An Agent Architecture to Design Self-Organizing Collectives:
- Principles and Application
- Evolving Preferences among Emergent Groups of Agents
- Structuring Agents for Adaptation
- Stochastic Simulation of Inherited Kinship-Driven Altruism
Theoretical Foundations of Adaptive Agents
- Learning in Multiagent Systems: An Introduction from a
- Game-Theoretic Perspective
- The Implications of Philosophical Foundations for Knowledge 22.
- Representation and Learning in Agents
- Using Cognition and Learning to Improve Agents' Reactions
- TTree: Tree-Based State Generalization with Temporally Abstract
- Actions
- Using Landscape Theory to Measure Learning Difficulty
- for Adaptive Agents
- Relational Reinforcement Learning for Agents in Worlds with Objects 29.
- Author Index

Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Eduardo Alonso, Daniel Kudenko, Dimitar Kazakov
Parution	12/06/2003
Nb. de pages	322
Format	16 x 24
Couverture	Relié
Poids	515g
Intérieur	Noir et Blanc
EAN13	9783540400684