New PDF release: Artificial Intelligence: Methodology, Systems, and

By Jérôme Euzenat, John Domingue

ISBN-10: 3540409300

ISBN-13: 9783540409304

This e-book constitutes the refereed court cases of the twelfth overseas convention on synthetic Intelligence: technique, platforms, and functions, AIMSA 2006, held in Varna, Bulgaria in September 2006.

The 28 revised complete papers provided including the abstracts of two invited lectures have been conscientiously reviewed and chosen from eighty one submissions. The papers are equipped in topical sections on brokers, constraints and optimization, person matters, choice aid, versions and ontologies, laptop studying, ontology manipulation, average language processing, and applications.

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Jérôme Euzenat, John Domingue's Artificial Intelligence: Methodology, Systems, and PDF

This booklet constitutes the refereed court cases of the twelfth foreign convention on man made Intelligence: technique, platforms, and purposes, AIMSA 2006, held in Varna, Bulgaria in September 2006. The 28 revised complete papers provided including the abstracts of two invited lectures have been conscientiously reviewed and chosen from eighty one submissions.

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Additional info for Artificial Intelligence: Methodology, Systems, and Applications: 12th International Conference, AIMSA 2006, Varna, Bulgaria, September 12-15, 2006, Proceedings

Example text

The logic of Prob is much 1 We disregard thus conflicts between different kinds of standards. OCC’s Emotions: A Formalization in a BDI Logic 27 weaker than the one of belief, in particular it is non-normal: the necessitation rule and the axiom K of belief operators do not have any counterpart in terms of Prob . Belief and Probability. They are related by the validity of: Bel i ϕ → Prob i ϕ (BPR) We define an abbreviation Expect i ϕ, reading “i believes that ϕ is probably true, but envisages the possibility that it could be false”.

In our encoding, FC is obtained naturally by applying the inference rule to the neighborhood of the current variable under instantiation. The rest of the paper is organized as follows: first we recall some background on both propositional calculus and discrete CSP formalisms. Then we introduce a general normal form which we use to express both SAT and CSP problems. After that, we define a new inference rule which we use to show results on arc consistency. We describe two enumerative algorithms (the FC and MAC versions) based on two different uses of the introduced inference rule.

Example 1. Let CDB =(±1, Bv0 Bv1 ) be a domain clause corresponding to the CSP variable B and CCi =(Av1 ∧ Bv0 ∧ Cv0 ) ∨ (Av0 ∧ Bv0 ∧ Cv1 ) a constraint clause corresponding to the CSP constraint Ci involving the variables {A, B, C}. The application of the rule IR on both clauses infers ¬Bv1 . (CDB ∧ CCi ¬Bv1 ) Proposition 1. The rule IR is sound (correct). Proof. Let Xv be a literal appearing in LD but not in LC and I a model of CCi ∧ CDX . CCi is a disjunction of conjunctions fi and each conjunction fi contains one literal of LD .

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Artificial Intelligence: Methodology, Systems, and Applications: 12th International Conference, AIMSA 2006, Varna, Bulgaria, September 12-15, 2006, Proceedings by Jérôme Euzenat, John Domingue


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