Adaptive Agents and Multi-Agent Systems III. Adaptation and by Ana L. C. Bazzan, Denise de Oliveira (auth.), Karl Tuyls,

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

This e-book includes chosen and revised papers of the eu Symposium on Adaptive and studying brokers and Multi-Agent structures (ALAMAS), variants 2005, 2006 and 2007, held in Paris, Brussels and Maastricht. The objective of the ALAMAS symposia, and this linked booklet, is to extend know-how and curiosity in version and studying for unmarried brokers and mul- agent structures, and inspire collaboration among laptop studying specialists, softwareengineeringexperts,mathematicians,biologistsandphysicists,andgive a consultant overviewof present nation of a?airs during this quarter. it truly is an inclusive discussion board the place researchers can current contemporary paintings and speak about their most recent principles for a ?rst time with their friends. Thesymposiaseriesfocusesonallaspectsofadaptiveandlearningagentsand multi-agent platforms, with a selected emphasis on how one can adjust proven studying concepts and/or create new studying paradigms to deal with the numerous demanding situations offered via advanced real-world difficulties. those symposia have been a very good luck and supplied a discussion board for the pres- tation of latest principles and effects concerning the perception of edition and studying for unmarried brokers and multi-agent structures. Over those 3 variants we bought fifty one submissions, of which 17 have been rigorously chosen, together with one invited paper of this year’s invited speaker Simon Parsons. this can be a very c- petitive attractiveness expense of roughly 31%, which, including overview cycles, has ended in a superb LNAI quantity. we are hoping that our readers may be encouraged by way of the papers integrated during this volume.

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Extra info for 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

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The Q-iteration algorithm using the approximation mapping (7) and projection mapping (8) can be written as Algorithm 1. To establish the equivalence between Algorithm 1 and the approximate Q-iteration in the form (6), observe that the right-hand side in line 4 of Algorithm 1 corresponds to [T (Qκ )](xi , uj ), where Qκ = F (θκ ). Hence, line 4 can be written θκ+1,i,j ← [P T F (θκ )]i,j and the entire for loop described by lines 3–5 is equivalent to (6). Algorithm 2 is a different version of fuzzy Q-iteration, that makes more efficient use of the updates by using the latest updated values of the parameters θ in each step of the computation.

Some suggestions are given by Kappen [4,5]. In the setting that we considered the model which describes the behaviour of the agents was given. It would be worthwhile, however, to consider cases of stochastic optimal control of multi-agent systems in continuous space and time where the model first needs to be learned. Acknowledgments We thank Joris Mooij for making available useful software and the reviewers for their useful remarks. This research is part of the Interactive Collaborative Information Systems (ICIS) project, supported by the Dutch Ministry of Economic Affairs, grant BSIK03024.

Section 8 outlines ideas for future work and concludes the paper. 1 Some authors use ‘model-based RL’ when referring to algorithms that build a model of the environment from interaction. We use the term ‘model-learning’ for such techniques, and reserve the name ‘model-based’ for algorithms that rely on an a priori model of the environment. Continuous-State Reinforcement Learning with Fuzzy Approximation 2 29 Reinforcement Learning In this section, the RL task is briefly introduced and its optimal solution is characterized.

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