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

Adaptation and Multi-Agent Learning, 5th, 6th, and 7th European Symposium, ALAMAS 2005-2007 on Adaptive and Learning Agents and Multi-Agent Systems, Revised Selected Papers

  • Conference proceedings
  • © 2008

Overview

Editors:
  1. Karl Tuyls
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  2. Ann Nowe
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  3. Zahia Guessoum
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  4. Daniel Kudenko
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 4865)

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

Included in the following conference series:

Conference proceedings info: AAMAS 2005, ALAMAS 2006. ALAMAS 2007.

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Table of contents (17 papers)

  1. Front Matter

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  2. To Adapt or Not to Adapt – Consequences of Adapting Driver and Traffic Light Agents

    • Ana L. C. Bazzan, Denise de Oliveira, Franziska Klügl, Kai Nagel
    Pages 1-14

  3. Optimal Control in Large Stochastic Multi-agent Systems

    • Bart van den Broek, Wim Wiegerinck, Bert Kappen
    Pages 15-26

  4. Continuous-State Reinforcement Learning with Fuzzy Approximation

    • Lucian Buşoniu, Damien Ernst, Bart De Schutter, Robert Babuška
    Pages 27-43

  5. Using Evolutionary Game-Theory to Analyse the Performance of Trading Strategies in a Continuous Double Auction Market

    • Kai Cai, Jinzhong Niu, Simon Parsons
    Pages 44-59

  6. Parallel Reinforcement Learning with Linear Function Approximation

    • Matthew Grounds, Daniel Kudenko
    Pages 60-74

  7. Combining Reinforcement Learning with Symbolic Planning

    • Matthew Grounds, Daniel Kudenko
    Pages 75-86

  8. Agent Interactions and Implicit Trust in IPD Environments

    • Enda Howley, Colm O’Riordan
    Pages 87-101

  9. Collaborative Learning with Logic-Based Models

    • Michal Jakob, Jan Tožička, Michal Pěchouček
    Pages 102-116

  10. Priority Awareness: Towards a Computational Model of Human Fairness for Multi-agent Systems

    • Steven de Jong, Karl Tuyls, Katja Verbeeck, Nico Roos
    Pages 117-128

  11. Bifurcation Analysis of Reinforcement Learning Agents in the Selten’s Horse Game

    • Alessandro Lazaric, Enrique Munoz de Cote, Fabio Dercole, Marcello Restelli
    Pages 129-144

  12. Bee Behaviour in Multi-agent Systems

    • Nyree Lemmens, Steven de Jong, Karl Tuyls, Ann Nowé
    Pages 145-156

  13. Stable Cooperation in the N-Player Prisoner’s Dilemma: The Importance of Community Structure

    • Colm O’Riordan, Humphrey Sorensen
    Pages 157-168

  14. Solving Multi-stage Games with Hierarchical Learning Automata That Bootstrap

    • Maarten Peeters, Katja Verbeeck, Ann Nowé
    Pages 169-187

  15. Auctions, Evolution, and Multi-agent Learning

    • Steve Phelps, Kai Cai, Peter McBurney, Jinzhong Niu, Simon Parsons, Elizabeth Sklar
    Pages 188-210

  16. Multi-agent Reinforcement Learning for Intrusion Detection

    • Arturo Servin, Daniel Kudenko
    Pages 211-223

  17. Networks of Learning Automata and Limiting Games

    • Peter Vrancx, Katja Verbeeck, Ann Nowé
    Pages 224-238

  18. Multi-agent Learning by Distributed Feature Extraction

    • Michael Wurst
    Pages 239-254

  19. Back Matter

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Other volumes

  1. Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning

Keywords

About this book

This book contains selected and revised papers of the European Symposium on Adaptive and Learning Agents and Multi-Agent Systems (ALAMAS), editions 2005, 2006 and 2007, held in Paris, Brussels and Maastricht. The goal of the ALAMAS symposia, and this associated book, is to increase awareness and interest in adaptation and learning for single agents and mul- agent systems, and encourage collaboration between machine learning experts, softwareengineeringexperts,mathematicians,biologistsandphysicists,andgive a representative overviewof current state of a?airs in this area. It is an inclusive forum where researchers can present recent work and discuss their newest ideas for a ?rst time with their peers. Thesymposiaseriesfocusesonallaspectsofadaptiveandlearningagentsand multi-agent systems, with a particular emphasis on how to modify established learning techniques and/or create new learning paradigms to address the many challenges presented by complex real-world problems. These symposia were a great success and provided a forum for the pres- tation of new ideas and results bearing on the conception of adaptation and learning for single agents and multi-agent systems. Over these three editions we received 51 submissions, of which 17 were carefully selected, including one invited paper of this year’s invited speaker Simon Parsons. This is a very c- petitive acceptance rate of approximately 31%, which, together with two review cycles, has led to a high-quality LNAI volume. We hope that our readers will be inspired by the papers included in this volume.

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