1996/01/01 by Salem Benferhat, Benferhat, Salem, Didier Dubois +3
Arts and Humanities · Computer Science · Psychology · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Epistemology, Ethics, and Metaphysics #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #Philosophy and Theoretical Science #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.1302.3559
openalex publication_date 1996/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Possibility theory offers a framework where both Lehmann's "preferential\ninference" and the more productive (but less cautious) "rational closure\ninference" can be represented. However, there are situations where the second\ninference does not provide expected results either because it cannot produce\nthem, or even provide counter-intuitive conclusions. This state of facts is not\ndue to the principle of selecting a unique ordering of interpretations (which\ncan be encoded by one possibility distribution), but rather to the absence of\nconstraints expressing pieces of knowledge we have implicitly in mind. It is\nadvocated in this paper that constraints induced by independence information\ncan help finding the right ordering of interpretations. In particular,\nindependence constraints can be systematically assumed with respect to formulas\ncomposed of literals which do not appear in the conditional knowledge base, or\nfor default rules with respect to situations which are "normal" according to\nthe other default rules in the base. The notion of independence which is used\ncan be easily expressed in the qualitative setting of possibility theory.\nMoreover, when a counter-intuitive plausible conclusion of a set of defaults,\nis in its rational closure, but not in its preferential closure, it is always\npossible to repair the set of defaults so as to produce the desired conclusion.\n