Stable Model Semantics in ProbLog and its Applications in Argumentation

Abstract

Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. A common assumption in probabilistic logic programming (PLP) is that probabilistic facts fully capture the uncertainty in the domain under investigation. Probabilistic argumentation problems, however, represent an interesting practical application where this is not always the case. In order to overcome this limitation, we present smProbLog, a novel PLP framework based on stable model semantics. smProbLog supports inference and learning also for programs where a choice of probabilistic facts does not yield a unique interpretation of the logical atoms. We show the convenience of this novel framework by encoding probabilistic argumentation problems as smProbLog programs. Approaching the problem from a PLP perspective allows us to apply PLP tools and algorithms to the domain of Argumentation, as probabilistic argumentation frameworks are not as expressive, flexible and rich in inference and learning algorithms as PLP.

Pietro Totis

Senior AI Research Scientist

My research interests include (probabilistic) logic programming, Knowledge Representation and Reasoning, NLP