Nonground Abductive Logic Programming with Probabilistic Integrity Constraints

Uncertain information is being taken into account in an increasing number of application fields. In the meantime, abduction has been proved a powerful tool for handling hypothetical reasoning and incomplete knowledge. Probabilistic logical models are suitable framework to handle uncertain information, last decade many probabilistic languages have proposed, as well inference learning systems them. realm Abductive Logic Programming (ALP), variety proof procedures defined well. this paper, we consider richer logic language, coping with variables. particular, ALP program enriched integrity constraints `a la IFF, possibly annotated probability value. We first present overall abductive its semantics according Distribution Semantics. then introduce procedure, obtained by extending one previously presented, prove soundness completeness.