2012/12/12 by Salem Benferhat, Benferhat, Salem, Didier Dubois +5
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge
paper · pdf · doi:10.48550/arxiv.1301.0555
openalex publication_date 2012/12/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recently, it has been emphasized that the possibility theory framework allows\nus to distinguish between i) what is possible because it is not ruled out by\nthe available knowledge, and ii) what is possible for sure. This distinction\nmay be useful when representing knowledge, for modelling values which are not\nimpossible because they are consistent with the available knowledge on the one\nhand, and values guaranteed to be possible because reported from observations\non the other hand. It is also of interest when expressing preferences, to point\nout values which are positively desired among those which are not rejected.\nThis distinction can be encoded by two types of constraints expressed in terms\nof necessity measures and in terms of guaranteed possibility functions, which\ninduce a pair of possibility distributions at the semantic level. A consistency\ncondition should ensure that what is claimed to be guaranteed as possible is\nindeed not impossible. The present paper investigates the representation of\nthis bipolar view, including the case when it is stated by means of conditional\nmeasures, or by means of comparative context-dependent constraints. The\ninterest of this bipolar framework, which has been recently stressed for\nexpressing preferences, is also pointed out in the representation of diagnostic\nknowledge.\n