Syntactic Computation of Hybrid Possibilistic Conditioning under Uncertain Inputs

Salem Benferhat 1 Célia Da Costa Pereira 2 Andrea G. B. Tettamanzi 3
2 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe KEIA
SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
3 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : We extend hybrid possibilistic conditioning to deal with inputs consisting of a set of triplets composed of propositional formulas, the level at which the formulas should be accepted, and the way in which their models should be revised. We characterize such conditioning using elementary operations on possibility distributions. We then solve a difficult issue that concerns the syntactic computation of the revision of possibilistic knowledge bases, made of weighted formulas, using hybrid conditioning. An important result is that there is no extra computational cost in using hybrid possibilistic conditioning and in particular the size of the revised possibilistic base is polynomial with respect to the size of the initial base and the input.
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Communication dans un congrès
IJCAI, Aug 2013, Beijing, China. AAAI, pp.6822, 2013
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Contributeur : Andrea G. B. Tettamanzi <>
Soumis le : mardi 19 novembre 2013 - 00:06:42
Dernière modification le : mercredi 8 avril 2015 - 16:34:22
Document(s) archivé(s) le : lundi 3 mars 2014 - 14:05:50

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Salem Benferhat, Célia Da Costa Pereira, Andrea G. B. Tettamanzi. Syntactic Computation of Hybrid Possibilistic Conditioning under Uncertain Inputs. IJCAI, Aug 2013, Beijing, China. AAAI, pp.6822, 2013. <hal-00905935>

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