Probabilistic Logic Programming under the L-Stable Semantics

Probabilistic logic programming extends logic programming to offer a rich specification language for statistical relational models and, more recently, to neurosymbolic reasoners. The standard stable model semantics adopted by probabilistic logic programs collapses in the presence of contradictions that can arise when knowledge is not carefully elicited. In this work, we study probabilistic disjunctive logic programs under the least-undefined stable model semantics. We prove missing complexity results for logic inference with bounded-arity predicates, and then derive the complexity of probabilistic inference with respect to both the credal and the maximum entropy semantics.

Conference paper