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Download Advances in Artificial Intelligence -- IBERAMIA 2014: 14th by Ana L.C. Bazzan, Karim Pichara PDF

By Ana L.C. Bazzan, Karim Pichara

This booklet constitutes the refereed complaints of the 14th Ibero-American convention on man made Intelligence, IBERAMIA 2014, held in Santiago de Chile, Chile, in November 2014. The sixty four papers awarded have been conscientiously reviewed and chosen from 136 submissions. The papers are prepared within the following topical sections: wisdom engineering, wisdom illustration and probabilistic reasoning making plans and scheduling usual language processing desktop studying fuzzy structures wisdom discovery and knowledge mining bio-inspired computing robotics imaginative and prescient multi-agent platforms agent-based modeling and simulation AI in schooling, affective computing, and human-computer interplay functions of AI and ambient intelligence.

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Additional info for Advances in Artificial Intelligence -- IBERAMIA 2014: 14th Ibero-American Conference on AI, Santiago de Chile, Chile, November 24-27, 2014, Proceedings

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Furthermore, since Proposition 2 ensures the NCE property independently of the preference criterion, we have obtained a family of semantics that soundly approximate the k-defeater semantics and ensure that no conflicting atoms can be entailed from a defeasible ontology. 6 Related Work Within Artificial Intelligence, many efforts to deal with inconsistent information have been developed in the last four decades. , a fact in D is assumed to be true if it can be assumed to be true. Argumentation [13, 15, 23, 27] has been used for handling uncertainty and inconsistency by means of reasoning about how contradictory arguments defeat each other.

We now adopt the definitions of counter-argument and attacks for defeasible Datalog± ontologies from [15]. First, an argument B, L is a sub-argument of A , L if B ⊆ A . Argument A1 , L1 counter-argues, rebuts, or attacks A2 , L2 at literal L, iff there exists a sub-argument A , L of A2 , L2 such that L and L1 conflict. Example 5. Consider derivation ∂ from Example 3 and let A be the set of (defeasible) atoms and (defeasible) TGDs used in ∂ . A is an argument for unstable(will). Also, we can obtain a minimal derivation ∂ for risky job(will) where B, the set of (defeasible) atoms and (defeasible) TGDs used in ∂ , is such that no conflicting atoms can be defeasibly derived from B ∪ ΣT .

A Datalog± ontology KB = (I, Σ), where Σ = ΣT ∪ ΣNC , consists of a database I, a set of TGDs ΣT , and a set of negative constraints ΣNC . , linear). Example 1 (used in the sequel as a running example) illustrates a simple Datalog± ontology. Example 1. V. Martinez et al. ΣT = {collaborates(P, A) → works in(A, P), in therapy(P) → unstable(P), lives depend on(A) ∧ works in(A, P) → risky job(P), psychiatrist(S, P) → in therapy(P), security agency(A) → lives depend on(A)}. 3 Defeasible Datalog± Ontologies Here we extend Datalog± ontologies to allow defeasible reasoning.

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