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

Adaptive Agents and Multi-Agent Systems Adaptation and Multi-Agent Learning / edited by Eduardo Alonso, Daniel Kudenko, Dimitar Kazakov.

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...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Alonso, Eduardo (Editor), Kudenko, Daniel (Editor), Kazakov, Dimitar (Editor)
Format: eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2003.
Edition:1st ed. 2003.
Series:Lecture Notes in Artificial Intelligence ; 2636
Springer eBook Collection.
Subjects:
Artificial intelligence.
Computer communication systems.
Software engineering.
Programming languages (Electronic computers).
Computer logic.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 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.

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