Alonso / Kudenko / Kazakov Adaptive Agents and Multi-Agent Systems

Adaptation and Multi-Agent Learning
2003
ISBN: 978-3-540-44826-6
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 330 Seiten, eBook

Reihe: Lecture Notes in Artificial Intelligence

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

Zielgruppe

Research

Autoren/Hrsg.

Alonso, Eduardo

Kudenko, Daniel

Kazakov, Dimitar

Inhaltsverzeichnis

Learning, Co-operation, and Communication.- Cooperative Multiagent Learning.- Reinforcement Learning Approaches to Coordination in Cooperative Multi-agent Systems.- 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 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.