smProbLog: Stable Model Semantics in ProbLog for Probabilistic Argumentation

TL;DR

This paper introduces smProbLog, a new probabilistic logic programming framework based on ProbLog, that models probabilistic argumentation more accurately by relaxing common assumptions, and demonstrates its effectiveness through experiments.

Contribution

It presents a novel interpretation of probabilistic argumentation as probabilistic logic programs, introduces a new semantics for these programs, and implements smProbLog for advanced reasoning tasks.

Findings

smProbLog effectively models probabilistic argumentation.

The new semantics allows for more flexible uncertainty representation.

Experimental results show acceptable computational costs.

Abstract

Argumentation problems are concerned with determining the acceptability of a set of arguments from their relational structure. When the available information is uncertain, probabilistic argumentation frameworks provide modelling tools to account for it. The first contribution of this paper is a novel interpretation of probabilistic argumentation frameworks as probabilistic logic programs. Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. We show that the programs representing probabilistic argumentation frameworks do not satisfy a common assumption in probabilistic logic programming (PLP) semantics, which is, that probabilistic facts fully capture the uncertainty in the domain under investigation. The second contribution of this paper is then a novel PLP semantics for programs where a choice of probabilistic facts does not uniquely determine the truth assignment of the logical atoms. The third contribution of this paper is the implementation of a PLP system supporting this semantics: smProbLog. smProbLog is a novel PLP framework based on the probabilistic logic programming language ProbLog. smProbLog supports many inference and learning tasks typical of PLP, which, together with our first contribution, provide novel reasoning tools for probabilistic argumentation. We evaluate our approach with experiments analyzing the computational cost of the proposed algorithms and their application to a dataset of argumentation problems.