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

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 reports 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 examine in all parts of man-made Intelligence and follow old AI recommendations to trendy challenge domain names in addition to fresh innovations to old challenge settings.

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Extra 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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The next definition captures the intuition that the relevant interpretations of a theory Γ are those that are δd,f -closest to Γ (see also [9]). Definition 4. The most plausible valuations of Γ (with respect to a pseudo distance d and an aggregation function f ) are defined as follows: Δd,f (Γ ) = ν ∈ Λ | ∀μ ∈ Λ δd,f (ν, Γ ) ≤ δd,f (μ, Γ ) Λ if Γ = ∅, otherwise. Distance-based entailments are now defined as follows: Definition 5. 1 Example 2. Let Γ = {p, ¬p, q}. As q is not related to the contradiction in Γ , there is no intuitive justification for concluding ¬q from Γ .

Definition 8. : d(ν, μ) = 0 kd if ν = μ, otherwise. Proposition 4. Let d be a uniform distance and Γ = {ψ1 , . . , ψn }. Then: Δd,max (Γ ) = mod(Γ ) if mod(Γ ) = ∅, Λ otherwise. Proof. If mod(Γ ) = ∅, then Δd,f (Γ ) = mod(Γ ) for every d and f (see [2]). Otherwise, there is at least on element in Γ that is not satisfiable, and so, for every valuation μ ∈ Λ, δd,max (μ, Γ ) = max{d(μ, ψ1 ), . . , d(μ, ψn )} = kd . It follows, then, that in this case Δd,max (Γ ) = Λ. Proposition 4 implies that for describing reasoning with uniform distances under the minmax strategy, it is enough to examine the drastic distance (and so henceforth we focus only on dU ): Corollary 2.

045). 1 Due to space limitations, we omit a figure to represent the information in the Table. 22 R. Alaiz-Rodr´ıguez and N. Japkowicz Table 5. Classifier performance under a Population drift with Not Represented cases in the training set. Error rate of the trivial classifier and a classifier trained with data from a NGP population are shown as well as the performance Deterioration (pD). 18 In summary, (a) all classifiers show a drop in performance when there are unrepresented cases in the training set, with (b) a slightly higher impact on simple classifiers than in complex ones.

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