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Adaptive Agents and Multi-Agent Systems II

Adaptation and Multi-Agent Learning

  • Conference proceedings
  • © 2005

Overview

Editors:
  1. Daniel Kudenko

    1. Department of Computer Science, University of York, York, UK

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  2. Dimitar Kazakov

    1. Artificial Intelligence Group, Department of Computer Science, University of York, Heslington, York, UK

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    You can also search for this editor in PubMed   Google Scholar

  3. Eduardo Alonso

    1. Department of Computing, City University, London, United Kingdom

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 3394)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: AAMAS 2003. AAMAS 2004.

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Table of contents (19 papers)

  1. Front Matter

    Download chapter PDF

  2. Gödel Machines: Towards a Technical Justification of Consciousness

    • Jürgen Schmidhuber
    Pages 1-23

  3. Postext – A Mind for Society

    • Avishalom Shalit, Tom Erez, Anna Deters, Uri Hershberg, Eran Shir, Sorin Solomon
    Pages 24-40

  4. Comparing Resource Sharing with Information Exchange in Co-operative Agents, and the Role of Environment Structure

    • Mark Bartlett, Dimitar Kazakov
    Pages 41-54

  5. Baselines for Joint-Action Reinforcement Learning of Coordination in Cooperative Multi-agent Systems

    • Martin Carpenter, Daniel Kudenko
    Pages 55-72

  6. SMART (Stochastic Model Acquisition with ReinforcemenT) Learning Agents: A Preliminary Report

    • Christopher Child, Kostas Stathis
    Pages 73-87

  7. Towards Time Management Adaptability in Multi-agent Systems

    • Alexander Helleboogh, Tom Holvoet, Danny Weyns, Yolande Berbers
    Pages 88-105

  8. Learning to Coordinate Using Commitment Sequences in Cooperative Multi-agent Systems

    • Spiros Kapetanakis, Daniel Kudenko, Malcolm J. A. Strens
    Pages 106-118

  9. Reinforcement Learning of Coordination in Heterogeneous Cooperative Multi-agent Systems

    • Spiros Kapetanakis, Daniel Kudenko
    Pages 119-131

  10. Evolving the Game of Life

    • Dimitar Kazakov, Matthew Sweet
    Pages 132-146

  11. The Strategic Control of an Ant-Based Routing System Using Neural Net Q-Learning Agents

    • David Legge
    Pages 147-166

  12. Dynamic and Distributed Interaction Protocols

    • Jarred McGinnis, David Robertson
    Pages 167-184

  13. Advice-Exchange Between Evolutionary Algorithms and Reinforcement Learning Agents: Experiments in the Pursuit Domain

    • Luís Nunes, Eugénio Oliveira
    Pages 185-204

  14. Evolving Strategies for Agents in the Iterated Prisoner’s Dilemma in Noisy Environments

    • Colm O’Riordan
    Pages 205-215

  15. Experiments in Subsymbolic Action Planning with Mobile Robots

    • John Pisokas, Ulrich Nehmzow
    Pages 216-229

  16. Robust Online Reputation Mechanism by Stochastic Approximation

    • Takamichi Sakai, Kenji Terada, Tadashi Araragi
    Pages 230-244

  17. Learning Multi-agent Search Strategies

    • Malcolm J. A. Strens
    Pages 245-259

  18. Combining Planning with Reinforcement Learning for Multi-robot Task Allocation

    • Malcolm Strens, Neil Windelinckx
    Pages 260-274

  19. Multi-agent Reinforcement Learning in Stochastic Single and Multi-stage Games

    • Katja Verbeeck, Ann Nowé, Maarten Peeters, Karl Tuyls
    Pages 275-294

  20. Towards Adaptive Role Selection for Behavior-Based Agents

    • Danny Weyns, Kurt Schelfthout, Tom Holvoet, Olivier Glorieux
    Pages 295-312
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  1. Adaptive Agents and Multi-Agent Systems II

Keywords

About this book

Adaptive agents and multi-agent systems is an emerging and exciting interdisciplinary area of research and development involving artificial intelligence, software engineering, and developmental biology, as well as cognitive and social science.

This book presents 17 revised and carefully reviewed papers taken from two workshops on the topic as well as 2 invited papers by leading researchers in the area. The papers deal with various aspects of machine learning, adaptation, and evolution in the context of agent systems and autonomous agents.

Editors and Affiliations

  • Department of Computer Science, University of York, York, UK

    Daniel Kudenko

  • Artificial Intelligence Group, Department of Computer Science, University of York, Heslington, York, UK

    Dimitar Kazakov

  • Department of Computing, City University, London, United Kingdom

    Eduardo Alonso

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