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New Semantics for Hybrid Probabilistic Programs

Hybrid probabilistic programs framework [5] is a variation of probabilistic annotated logic programming approach, which allows the user to explicitly encode the available knowledge about the dependency among the events in the program. In this paper, we extend the language of hybrid probabilistic programs by allowing disjunctive composition functions to be associated with heads of clauses and change its semantics to be more suitable for real-life applications. We show on a probabilistic AI planning example that the new semantics allows us to obtain more intuitive and accurate probabilities. The new semantics of hybrid probabilistic programs subsumes Lakshmanan and Sadri [17] framework of probabilistic logic programming. The fixpoint operator for the new semantics is guaranteed to be always continuous. This is not the case in the probabilistic annotated logic programming in general and the hybrid probabilistic programs framework in particular.