Download 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.)

This booklet constitutes the refereed lawsuits of the twentieth 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 conscientiously reviewed and chosen from 260 submissions. The papers are equipped in topical part on brokers, bioinformatics, type, constraint delight, info mining, wisdom illustration and reasoning, studying, average language, and planning.

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Read or Download 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 PDF

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Extra info 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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Technical Report, Stanford Digital Library Technologies Project (1998) 17. : Attack-resistance of computational trust models. In Proc. of the 12th IEEE Int. ca Abstract. Agent competition and coordination are two classical and most important tasks in multiagent systems. In recent years, there was a number of learning algorithms proposed to resolve such type of problems. Among them, there is an important class of algorithms, called adaptive learning algorithms, that were shown to be able to converge in self-play to a solution in a wide variety of the repeated matrix games.

Another metric Absolute Prediction Shift measured the distortion of prediction occurring due to an attack. While p_rating is the predicted rating computed before an attack, p_rating′ means the predicted rating computed after an attack [14]. APS = ∑ M i =1 ′ | p _ rating i − p _ rating i | (7) M The evaluation value of Prediction Shift originally has two meanings, in other words, a positive value has different meaning from a negative value. Each value means that the attack has succeeded in making the target item more positively or negatively rated [14].

The other two algorithms, namely Policy Hill Climbing [2] and Adaptive Play Q-learning [3] have already been adapted to the stochastic game setting by their respective authors. These algorithms was proven to converge to an equilibrium in self-play in the repeated matrix games, but, to our knowledge, they were never compared with each other in the case of stochastic games. This encouraged us to do this research. The goals we aimed were to investigate these algorithms in detail and to make a preliminary conclusion about their performance in stochastic games when playing against each other.

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