Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning: Adaptation and Multi-Agent Learning, 5th, 6th, and 7th European Symp (Paperback or Softback). Questo articolo non è disponibile.Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning: Adaptation and Multi-Agent Learning, 5th, 6th, and 7th European Symp (Paperback or Softback)

This book constitutes the thoroughly refereed post-conference proceedings of three editions of the European Symposium on Adaptive and Learning Agents and Multi-Agent Systems, ALAMAS, held in Paris, Brussels and Maastricht in 2005, 2006, and 2007 respectively. This book presents 17 revised and carefully reviewed papers selected from 51 total submissions to the ALAMAS symposia from 2005 to 2007. This volume aims at increasing awareness and interest in adaptation and learning for single agents and multi-agent systems, and encourages collaboration between Machine Learning experts, Software Engineering experts, Mathematicians, Biologists and Physicists, and gives a representative overview of current state of affairs in this area.

To Adapt or Not to Adapt – Consequences of Adapting Driver and Traffic Light Agents.- Optimal Control in Large Stochastic Multi-agent Systems.- Continuous-State Reinforcement Learning with Fuzzy Approximation.- Using Evolutionary Game-Theory 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 Logic-Based Models.- Priority Awareness: Towards a Computational Model of Human Fairness for Multi-agent Systems.- Bifurcation Analysis of Reinforcement Learning Agents in the Selten’s Horse Game.- Bee Behaviour in Multi-agent Systems.- Stable Cooperation in the N-Player Prisoner’s Dilemma: The Importance of Community Structure.- Solving Multi-stage Games with Hierarchical Learning Automata That Bootstrap.- Auctions, Evolution, and Multi-agent Learning.- Multi-agent Reinforcement Learning for Intrusion Detection.- Networks of Learning Automata and Limiting Games.- Multi-agent Learning by Distributed Feature Extraction.