Download e-book for iPad: Advances in Artificial Intelligence: 24th Canadian by Eric Aaron, Juan Pablo Mendoza (auth.), Cory Butz, Pawan

By Eric Aaron, Juan Pablo Mendoza (auth.), Cory Butz, Pawan Lingras (eds.)

ISBN-10: 3642210422

ISBN-13: 9783642210426

This publication constitutes the refereed complaints of the twenty fourth convention on synthetic Intelligence, Canadian AI 2011, held in St. John’s, Canada, in might 2011. The 23 revised complete papers offered including 22 revised brief papers and five papers from the graduate pupil symposium have been rigorously reviewed and chosen from eighty one submissions. The papers disguise a wide diversity of issues proposing unique paintings in all parts of man-made intelligence, both theoretical or applied.

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Additional info for Advances in Artificial Intelligence: 24th Canadian Conference on Artificial Intelligence, Canadian AI 2011, St. John’s, Canada, May 25-27, 2011. Proceedings

Example text

The main advantage of our approach is that it can cover a potentially unrestricted range of semantic relations while most supervised and semi-supervised approaches can learn to extract only those relations that have been exemplified in annotated text, seed patterns. Our assumption for Relation Extraction (RE) is that it is between NE’s stated in the same sentence and that presence or absence of relation is independent of the text prior to or succeeding the sentence. Moreover, our approach is suitable in situations where a lot of unannotated text is available as it does not require manually annotated text or seeds.

In haplotype inference problem, we are given an integer r and a set, G, consisting of n strings in {0, 1, 2}m, for a fixed m. We are asked if there exists a set of r strings, H, in {0, 1}m such that for every g ∈ G there are two strings in H which explain g. We say two strings h1 and h2 explain an string g iff for every position 1 ≤ i ≤ m either g[i] = h1 [i] = h2 [i] or g[i] = 2 and h1 [i] = h2 [i]. The following axiomatization is intentionally produced in a way to generate the same CNF encoding as presented in [7] in the assumption that the gadget used for count is a simplified adder circuit [7].

98. Ios Pr. , Amsterdam (2006) 4. : Grounding for model expansion in k-guarded formulas with inductive definitions. In: Proc. IJCAI 2007, pp. 161–166 (2007) 5. : A method for solving NP search based on model expansion and grounding. Master’s thesis, Simon Fraser University (2006) 6. : On the complexity of derivation in propositional calculus. Studies in constructive mathematics and mathematical logic 2(115-125), 10–13 (1968) 7. : Efficient haplotype inference with boolean satisfiability. In: AAAI.

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Advances in Artificial Intelligence: 24th Canadian Conference on Artificial Intelligence, Canadian AI 2011, St. John’s, Canada, May 25-27, 2011. Proceedings by Eric Aaron, Juan Pablo Mendoza (auth.), Cory Butz, Pawan Lingras (eds.)


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