Advances in Artificial Intelligence: 26th Canadian by Mohammad Ali Bagheri, Qigang Gao, Sergio Escalera (auth.),

By Mohammad Ali Bagheri, Qigang Gao, Sergio Escalera (auth.), Osmar R. Zaïane, Sandra Zilles (eds.)

This ebook constitutes the refereed complaints of the twenty fifth Canadian convention on man made Intelligence, Canadian AI 2012, held in Regina, SK, Canada, in could 2013. The 17 typical papers and 15 brief papers awarded have been rigorously reviewed and chosen from seventy three preliminary submissions and are observed by way of eight papers from the Graduate scholar Symposium that have been chosen from 14 submissions. The papers disguise quite a few issues inside AI, corresponding to: details extraction, wisdom illustration, seek, textual content mining, social networks, temporal associations.

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Additional info for Advances in Artificial Intelligence: 26th Canadian Conference on Artificial Intelligence, Canadian AI 2013, Regina, SK, Canada, May 28-31, 2013. Proceedings

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34 K. Ghazinou, M. Sokolova, and S. Matwin We also measured the ratio of the number of times that the term was used in MySpace and the number of times that revealed PHI. We called the ratio the Risk Factor of Personal Information (RFPI). In other words, for a term t, RFPIt is: RFPIt = number of times t reveals PHI / number of times t appears in a text Table 7 illustrates the top RFPI terms from MedDRA and SNOMED that often reveal PHI. There is an overlap between the top most used terms of MedDRA and SNOMED with highest RFPI.

The operators are written in the form LHS → RHS where LHS is a vector of length three defining the operator’s preconditions and RHS is a vector of length three defining its effects. The LHS may contain variable symbols (Xi in this example); when the operator is applied to a state, the variable symbols are bound to the value of the state in the corresponding position. Preconditions test either that the state has a specific value for a state variable or that the value of two or more state variables are equal (this is done by having the same Xi occur in all the positions that are being tested for equality).

On the other hand, duplicate detection is useful to add to a system that does move pruning because move pruning, in general, is incomplete: it only detects short sequences that are redundant (in the current implementation move pruning considers all and only sequences of length L or less) and it only detects “universal” redundancy, as opposed to “serendipitous” redundancy, as illustrated in the example above, where sequences A and B are redundant when applied to certain states but are not redundant in general.

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