Towards the computation of stable probabilistic model semantics
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
Hybrid probabilistic logic programs with non-monotonic negation
ICLP'05 Proceedings of the 21st international conference on Logic Programming
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This dissertation is divided into two parts. The first part presents a general overview of logic programming with uncertainty in general and probabilistic logic programming in particular. The second part describes the development of more sophisticated semantics for hybrid probabilistic logic programs, the most expressive framework in probabilistic annotated logic programming, along with its extension to cope with non-monotonic negation. The extension of hybrid probabilistic programs is presented by providing stable model semantics and well-founded semantics for hybrid probabilistic programs with non-monotonic negation. In addition, an algorithm for computing the stable model semantics for hybrid probabilistic programs with non-monotonic negation is proposed.