Adaptive Agents and Multi-Agent Systems III. 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

Ana L. C. Bazzan, Denise de Oliveira (auth.), Karl Tuyls, Ann Nowe, Zahia Guessoum, Daniel Kudenko (eds.)3540779477, 9783540779476

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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Table of contents :
Front Matter….Pages –
To Adapt or Not to Adapt – Consequences of Adapting Driver and Traffic Light Agents….Pages 1-14
Optimal Control in Large Stochastic Multi-agent Systems….Pages 15-26
Continuous-State Reinforcement Learning with Fuzzy Approximation….Pages 27-43
Using Evolutionary Game-Theory to Analyse the Performance of Trading Strategies in a Continuous Double Auction Market….Pages 44-59
Parallel Reinforcement Learning with Linear Function Approximation….Pages 60-74
Combining Reinforcement Learning with Symbolic Planning….Pages 75-86
Agent Interactions and Implicit Trust in IPD Environments….Pages 87-101
Collaborative Learning with Logic-Based Models….Pages 102-116
Priority Awareness: Towards a Computational Model of Human Fairness for Multi-agent Systems….Pages 117-128
Bifurcation Analysis of Reinforcement Learning Agents in the Selten’s Horse Game….Pages 129-144
Bee Behaviour in Multi-agent Systems….Pages 145-156
Stable Cooperation in the N-Player Prisoner’s Dilemma: The Importance of Community Structure….Pages 157-168
Solving Multi-stage Games with Hierarchical Learning Automata That Bootstrap….Pages 169-187
Auctions, Evolution, and Multi-agent Learning….Pages 188-210
Multi-agent Reinforcement Learning for Intrusion Detection….Pages 211-223
Networks of Learning Automata and Limiting Games….Pages 224-238
Multi-agent Learning by Distributed Feature Extraction….Pages 239-254
Back Matter….Pages –