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 ebook constitutes the refereed court cases of the twentieth convention of the Canadian Society for Computational reports of Intelligence, Canadian AI 2007, held in Montreal, Canada, in could 2007.

The forty six revised complete papers offered have been rigorously reviewed and chosen from 260 submissions. The papers are prepared in topical part on brokers, bioinformatics, type, constraint delight, info mining, wisdom illustration and reasoning, studying, usual 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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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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N ), where n is the number of players, Aj is the strategy space of player j, j = 1 . . n, and the value function Rj : A1 × An → R defines the utility for player j of a joint action a ∈ A = A1 × . . × An . A mixed strategy for player j is a distribution π j , where πaj j is the probability for player j to select some action aj . A strategy is pure if πaj j = 1 for some aj . A strategy profile is a collection Π = {π j |j = 1 . . n} of all players’ strategies. A reduced profile for player j, Π−j = Π\{π j }, is a strategy profile containing strategies of all players except j, and Π−j a−j is the probability for players k = j to play a joint action a−j ∈ A−j = A1 × .

Then, this dataset was divided into two parts; training set contains all of each user’s ratings except one rating used for testing. Testing set contains the only one rating for each user. In order to measure performance, Mean Absolute Error, which is used as a measure of how accurate prediction of user’s rating for an item can be, was performed [1, 5, 8]. MAE of all users in the testing set is defined as: − MAE = ∑ M i =1 | p _ rating i − a _ rating i | (6) M where M is a list of all items and is the actual/predicted rating pairs of each user in the testing set.

By pruning the successive nodes, tracing costs can be decreased drastically. 3 Propagating User Feedback Based on this similarity model, AGENTPo provides recommendations to PO. Simply, the particular items, which obtain either the highest averages of each column or the highest prediction values, are recommended. (5) [1, 5]. p _ rating Po , IPj = ∑ IOi ∈IO {sim( IOi , IPj ) × Rating Po , IOi } ∑ IOi ∈IO (5) sim( IOi , IPj ) The idea is that the average rating of items that are similar to the selected item is a good estimate of the rating for the selected item [1].

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