Adaptive Agents and Multi-Agent Systems
Editat de Eduardo Alonso, Daniel Kudenko, Dimitar Kazakoven Limba Engleză Paperback – 23 apr 2003
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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Specificații
ISBN-13: 9783540400684
ISBN-10: 3540400680
Pagini: 344
Ilustrații: XIV, 330 p.
Dimensiuni: 155 x 235 x 19 mm
Greutate: 0.52 kg
Ediția:2003
Editura: Springer
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3540400680
Pagini: 344
Ilustrații: XIV, 330 p.
Dimensiuni: 155 x 235 x 19 mm
Greutate: 0.52 kg
Ediția:2003
Editura: Springer
Locul publicării:Berlin, Heidelberg, Germany
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Public țintă
ResearchCuprins
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.