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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 booklet constitutes the refereed court cases of the twentieth convention of the Canadian Society for Computational experiences of Intelligence, Canadian AI 2007, held in Montreal, Canada, in might 2007.

The forty six revised complete papers provided have been conscientiously reviewed and chosen from 260 submissions. The papers are prepared in topical part on brokers, bioinformatics, category, constraint pride, facts mining, wisdom illustration and reasoning, studying, normal 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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The consolidated operational labor cost is heavily depends on driving, riding, and walking distance which define the distance between every pair of locations. Driving and riding distances can be approximately calculated as a rectilinear distance because a general buffer is filled with vehicles, and vehicles and vans can move only through the accessible roads in the yard. For example, driving and riding distances between two locations α and β, dD(α, β) and dR(α, β), are calculated by d D (α , β ) = d R (α , β ) = X α − X β + Y α − Y β where Xi and Yi represent X-coordinate and Y-coordinate of location i, respectively.

Recently, there was a number of learning algorithms proposed to resolve decision problems in stochastic games [1,2,3,4,5,6,7,8,9,10,11]. Typically, these algorithms are constructed to iteratively play a game with an opponent, and, by playing this game, to converge to a solution. Solution in game theory is called equilibrium. We say that the playing strategies of all agents forms an equilibrium in a stochastic game if a unilateral deviation of an agent from its current strategy contradicts its principles of rationality (usually, maximization of the utility).

Through the prior experiment and previous research, we selected 50 as a neighborhood size and set it up in this experiment [1, 9]. In both cases of TCFMA + cosine-based and random model building, the average number of manipulated users accessed for building each user’s model was measured and the distortion of prediction occurring due to these data was compared by using Absolute Prediction Shift. 0 100 500 1000 2000 The Number of Injected Manipulated Users Fig. 4. Comparison of robustness on manipulated users In accordance with increases in the number of manipulated users, we find that the proposed method showed better results than random model building in both measurements.

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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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