Towards a More Practical Hybrid Probabilistic Logic Programming Framework

The hybrid probabilistic programs framework [1] allows the user to explicitly encode both logical and statistical knowledge available 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 we modify its semantics to make it more suitable to encode real-world applications. The new semantics is a natural extension of standard logic programming semantics. The new semantics of hybrid probabilistic programs also subsumes the implication-based probabilistic approach proposed by Lakshmanan and Sadri [12]. We provide also a sound and complete algorithm to compute the least fixpoint of hybrid probabilistic programs with annotated atomic formulas as rule heads.