PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Loyer Y., Straccia U. Default Knowledge in Logic Programs with Uncertainty. In: 19th Int. Conf., ICLP 2003 (Mumbai, India, 9-13 Dicember 2003). Proceedings, pp. 466 - 480. C. Palamidessi (ed.). (Lecture Notes in Computer Science, vol. 2916). Springer Verlag, 2003.
 
 
Abstract
(English)
Many frameworks have been proposed to manage uncertain information in logic programming. Essentially, they differ in the underlying notion of uncertainty and how these uncertainty values, associated to rules and facts, are managed. The goal of this paper is to allow the reasoning with non-uniform default assumptions, i.e with any arbitrary assignment of default values to the atoms. Informally, rather than to rely on the same default certainty value for all atoms, we allow arbitrary assignments to complete information. To this end, we define both epistemologically and computationally the semantics according to any given assumption. For reasons of generality, we present our work in the framework presented in [Lakshmanan01] as a unifying umbrella for many of the proposed approaches to the management of uncertainty in logic programming. Our extension is conservative in the following sense: (i) if we restrict our attention to the usual uniform Open World Assumption, then the semantics reduces to the Kripke-Kleene semantics, and (ii) if we restrict our attention to the uniform Closed World Assumption, then our semantics reduces to the well-founded semantics.
Subject Uncertainty
Bilattices
Fixed-point semantics
Logic programs
I.2.3:Artificial Intelligence:Deduction and Theorem Proving.Logic


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