Download e-book for kindle: Advances in Artificial Intelligence: 20th Conference of the by Yu Zhang (auth.), Ziad Kobti, Dan Wu (eds.)

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

ISBN-10: 3540726640

ISBN-13: 9783540726647

ISBN-10: 3540726659

ISBN-13: 9783540726654

This publication constitutes the refereed court cases of the 20 th convention of the Canadian Society for Computational reports of Intelligence, Canadian AI 2007, held in Montreal, Canada, in may perhaps 2007.

The forty six revised complete papers offered have been rigorously reviewed and chosen from 260 submissions. The papers are equipped in topical part on brokers, bioinformatics, category, constraint pride, info mining, wisdom illustration and reasoning, studying, average language, and planning.

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Additional 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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If transition is successful, robot changes the cell in the intended direction. Otherwise, robot keeps its current position. For each action made in each cell, except the goal cell, robot 32 A. Burkov, A. Boularias, and B. Chaib-draa 1 2 2 1 1 2 2 (a) (b) Fig. 1. (a) The two-robot-on-the-grid coordination problem and (b) The two-robotpredator-prey competition game receives a negative reward. A collision is possible if robots are trying to transit into the same cell or to trade cells. In the case of collision, robots receive a negative collision reward.

They called their approach Infinitesimal Gradient Ascent (IGA) [4]. Unlike APQ, which can learn and play pure strategies only, IGA players were designed to be capable to learn and play mixed strategies. The problem of the gradient ascent in matrix games was modelled by Singh and colleagues as having two payoff matrices for the row and column players, r and c, as follows: r r c c Rr = 11 12 , Rc = 11 12 r21 r22 c21 c22 If row player r selects an action i and the column player c selects an action j, r c and Rij respectively.

In this section we analyze in detail these algorithms. Also, we introduce a new multiagent learning algorithm, called ModIGA. , APQ) and which, unlike IGA, can be applied to the games with more than two actions. 1 Adaptive Play Q-Learning Formally, each player j playing Adaptive Play [12] saves in memory a history −j Htj = {a−j t−p , . . , at } of the last p joint actions played by the other players. To select a strategy to play at time t + 1 each player randomly and irrevocably samˆ tj = {a−j , .

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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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