PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Sebastiani F. Conditional probabilistic reasoning without conditional logic. In: AIICSR - Artificial intelligence and information-control systems of robots '97 : proceedings of the Seventh International Conference on Artificial Intelligence and Information-Control Systems of Robots (Smolenice Castle, Slovakia, 10-14 September 1997). Proceedings, pp. 299 - 310. Ivan Planter (ed.). World Scientific, 1997.
 
 
Abstract
(English)
Imaging is a class of non-Bayesian methods for the revision of probability density functions originally proposed as a semantics for conditional logic. Two of this revision functions, Standard Imaging and General Imaging have successfully been applied to modelling information retrieval (IR). Due to the problematic nature of a "direct" implementation of Imaging revision functions, we propose thei alternative implementaion by representing the semantic structure that underlies them, in the language of a probalistic (Bayesian) logic. Recasting this models of information retrieval in such a general-purpose Knowledge representation (KR) tool, be sides showing the potential of this "Bayesian" tool for the representation of non-Bayesian revision functions, paves the way to a possible integration of this models with other more KR-oriented modes of IR, and to the exploitation of general purpose domain-knowledge.
Subject Probabilistic reasoning


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