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

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Karl Tuyls, Ann Nowe, Zahia Guessoum, Daniel Kudenko
Springer Science & Business Media, Feb 8, 2008 - Computers - 258 pages
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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To Adapt or Not to Adapt Consequences of Adapting Driver and Traffic Light Agents
Optimal Control in Large Stochastic Multiagent Systems
ContinuousState Reinforcement Learning with Fuzzy Approximation
Using Evolutionary GameTheory to Analyse the Performance of Trading Strategies in a Continuous Double Auction Market
Parallel Reinforcement Learning with Linear Function Approximation
Combining Reinforcement Learning with Symbolic Planning
Agent Interactions and Implicit Trust in IPD Environments
Collaborative Learning with LogicBased Models
Bee Behaviour in Multiagent Systems
The Importance of Community Structure
Solving Multistage Games with Hierarchical Learning Automata That Bootstrap
Auctions Evolution and Multiagent Learning
Multiagent Reinforcement Learning for Intrusion Detection
Networks of Learning Automata and Limiting Games
Multiagent Learning by Distributed Feature Extraction
Author Index

Towards a Computational Model of Human Fairness for Multiagent Systems
Bifurcation Analysis of Reinforcement Learning Agents in the Seltens Horse Game

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