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

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

This ebook constitutes the refereed court cases of the twenty first convention of the Canadian Society for Computational reviews of Intelligence, Canadian AI 2008, held in Windsor, Canada, in could 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 fine quality learn in all parts of man-made Intelligence and follow historic AI concepts to trendy challenge domain names in addition to contemporary options to historic challenge settings.

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Additional resources for 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

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Ai−1 , ai+1 , . . , am ∈ {t, f }}, mod(¬ψ) = Λ \ mod(ψ), mod(ψ ∧ ϕ) = mod(ψ) ∩ mod(ϕ), mod(ψ ∨ ϕ) = mod(ψ) ∪ mod(ϕ). For a theory Γ = {ψ1 , . . , ψn } 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).

N Definition 11. A formula ψ is i-validated for i ∈ N+ , if among the 2|Atoms(ψ)| |Atoms(ψ)| valuations on Atoms(ψ), at most Ki valuations do not satisfy ψ. Note 4. An i-validated formula ψ is also j-validated, for 1 ≤ i ≤ j ≤ |Atoms(ψ)|. Example 5. Any tautology is 1-validated (thus it is i-validated for any i). , an atomic formula or its negation) is 1-validated. Moreover, as a disjunction of literals is either a tautology or is falsified by only one valuation, Lemma 2. Every clause is 1-validated.

Arieli and A. Zamansky 3 MinMax Reasoning In this section we study distance-based reasoning by min-max methods, that is: minimization of maximal distances. This kind of reasoning may be viewed as a skeptical approach, since it minimizes worst cases (maximal distances). Distance entailments of this type are induced by the max aggregation function. 1 Inductively Representable Distances Min-max distance-based reasoning is characterized as follows: 2 Proposition 2. For any pseudo distance d and theory Γ = {ψ1 , .

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