2013/03/27 by Didier Dubois, Jerome Lang, Dubois, Didier +4
Computer Science · #Advanced Algebra and Logic #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #Semantic Web and Ontologies #cs.AI
paper · pdf · doi:10.48550/arxiv.1304.1500
Appears in Proceedings of the Fifth Conference on Uncertainty in Artificial Intelligence (UAI1989)
arxiv created 2013/03/27 · openalex publication_date 2013/03/27 · arxiv updated 2013/04/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper an approach to automated deduction under uncertainty,based on possibilistic logic, is proposed ; for that purpose we deal with clauses weighted by a degree which is a lower bound of a necessity or a possibility measure, according to the nature of the uncertainty. Two resolution rules are used for coping with the different situations, and the refutation method can be generalized. Besides the lower bounds are allowed to be functions of variables involved in the clause, which gives hypothetical reasoning capabilities. The relation between our approach and the idea of minimizing abnormality is briefly discussed. In case where only lower bounds of necessity measures are involved, a semantics is proposed, in which the completeness of the extended resolution principle is proved. Moreover deduction from a partially inconsistent knowledge base can be managed in this approach and displays some form of non-monotonicity.