Advances in Artificial Intelligence: 20th Conference of the by Yu Zhang (auth.), Ziad Kobti, Dan Wu (eds.) PDF

By Yu Zhang (auth.), Ziad Kobti, Dan Wu (eds.)

ISBN-10: 3540726640

ISBN-13: 9783540726647

ISBN-10: 3540726659

ISBN-13: 9783540726654

This booklet constitutes the refereed complaints of the twentieth convention of the Canadian Society for Computational experiences of Intelligence, Canadian AI 2007, held in Montreal, Canada, in may well 2007.

The forty six revised complete papers offered have been conscientiously reviewed and chosen from 260 submissions. The papers are prepared in topical part on brokers, bioinformatics, class, constraint pride, information mining, wisdom illustration and reasoning, studying, traditional language, and planning.

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Extra resources for Advances in Artificial Intelligence: 20th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2007, Montreal, Canada, May 28-30, 2007. Proceedings

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Similar equations can be written for the column player c as well. Obviously, the opponent’s mixed strategy is supposed to be known by the players. Singh and colleagues proved the convergence of IGA to an equilibrium (or, at least, to the equivalent average reward of an equilibrium), when played in self-play, in the case of the infinitesimal step size (limη→0 ). 3 Policy Hill-Climbing Algorithm The first practical algorithm capable to play mixed strategies that realized the convergence properties of IGA was Policy Hill-Climbing (PHC) learning algorithm [2].

However, if the other players are learning, the PHC algorithm may not converge to a stationary policy though its average reward will converge to the reward of a Nash equilibrium [2]. 4 31 ModIGA While IGA demonstrated good convergence results, its applicability in reality is limited to the two-action case where the opponent is playing an identifiable mixed strategy. This assumption does not reflect real nature problems. In reality, we are usually expecting agent to observe the opponent’s actions rather than its mixed strategy.

Although the algorithms of the third group are very interesting and empirically shown to have several attractive properties, such as exploiting their opponents in adversarial games [10,11] and converging to a solution maximizing welfare of both players in non-adversarial two-player matrix games [11], there are still no theoretical proofs of their correctness, while in the first two groups there are algorithms that were formally proven to have such properties as rationality and convergence. In our analysis, we opted for the following three adaptive learning algorithms: Infinitesimal Gradient Ascent (IGA) [4], Policy Hill-Climbing (PHC) [2] and Adaptive Play Q -learning (APQ) [3] because, as we have just noted, (1) they are theoretically proven to converge to a stable solution (at least in selfplay), (2) they represent two major classes of learning algorithms, those able to play pure strategies only (APQ) and those able to play mixed strategies (IGA, PHC).

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Advances in Artificial Intelligence: 20th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2007, Montreal, Canada, May 28-30, 2007. Proceedings by Yu Zhang (auth.), Ziad Kobti, Dan Wu (eds.)


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