Rocío Alaiz-Rodríguez (auth.), Sabine Bergler (eds.)'s Advances in Artificial Intelligence: 21st Conference of the PDF

By Rocío Alaiz-Rodríguez (auth.), Sabine Bergler (eds.)

ISBN-10: 3540688218

ISBN-13: 9783540688211

ISBN-10: 3540688250

ISBN-13: 9783540688259

This booklet constitutes the refereed complaints of the twenty first convention of the Canadian Society for Computational reviews of Intelligence, Canadian AI 2008, held in Windsor, Canada, in may possibly 2008.

The 30 revised complete papers provided including five revised brief papers have been conscientiously reviewed and chosen from seventy five submissions. The papers current unique high quality learn in all components of synthetic Intelligence and observe ancient AI concepts to fashionable challenge domain names in addition to fresh ideas to historic challenge settings.

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Xn }) = 0 iff x1 = x2 = . . xn = 0, and (iii) f ({x}) = x for every x ∈ R. , summation, average, maximum, and so forth. Notation 1. Given a finite set S and a (pseudo) distance d, denote: maxd S = max{d(s1 , s2 ) | s1 , s2 ∈ S}. Definition 3. Given a theory Γ = {ψ1 , . . , ψn }, a valuation ν ∈ Λ, a pseudodistance d, and an aggregation function f , define: – d(ν, ψi ) = min{d(ν, μ) | μ ∈ mod(ψi )} if mod(ψi ) = ∅, 1 + maxd Λ otherwise. – δd,f (ν, Γ ) = f ({d(ν, ψ1 ), . . , d(ν, ψn )}). Note 1.

The paper is structured as follows. In Section 2, we provide the reader with a review of the state of the art of the field. In Section 3, we describe the methods we used in our experiments. The results of the experiments are presented in Section 4. Section 5 describes how our results will be applied to the eNulog program. Finally, in Section 6, we leave the reader with some concluding thoughts and future work that could be undertaken. 2 Related Work Typically, within sentiment analysis classification, there are two main avenues of research.

N } we define mod(Γ ) = mod(ψ1 ) ∩ . . ∩ mod(ψn ). For defining distance-based entailments we recall the definitions in [1,2]. Definition 1. A pseudo-distance on U is a total function d : U× U → N so that for all ν, μ ∈ U d(ν, μ) = d(μ, ν) (symmetry), and d(ν, μ) = 0 iff ν = μ (identity preservation). A distance (metric) d on U is a pseudo-distance that satisfies the triangular inequality: for all ν, μ, σ ∈ U , d(ν, σ) ≤ d(ν, μ) + d(μ, σ). 38 O. Arieli and A. Zamansky Example 1. It is easy to verify that the following functions are distances on the space Λ of two-valued valuations on Atoms: – The drastic distance: dU (ν, μ) = 0 if ν = μ and dU (ν, μ) = 1 otherwise.

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Advances in Artificial Intelligence: 21st Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2008 Windsor, Canada, May 28-30, 2008 Proceedings by Rocío Alaiz-Rodríguez (auth.), Sabine Bergler (eds.)


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