Understanding ProbLog as Probabilistic Argumentation

Francesca Toni
(Department of Computing, Imperial College London, UK)
Nico Potyka
(Department of Computing, Imperial College London, UK)
Markus Ulbricht
(Department of Computer Science, Leipzig University, Germany)
Pietro Totis
(Department of Computer Science, KU Leuven, Belgium)

ProbLog is a popular probabilistic logic programming language/tool, widely used for applications requiring to deal with inherent uncertainties in structured domains. In this paper we study connections between ProbLog and a variant of another well-known formalism combining symbolic reasoning and reasoning under uncertainty, i.e. probabilistic argumentation. Specifically, we show that ProbLog is an instance of a form of Probabilistic Abstract Argumentation (PAA) that builds upon Assumption-Based Argumentation (ABA). The connections pave the way towards equipping ProbLog with alternative semantics, inherited from PAA/PABA, as well as obtaining novel argumentation semantics for PAA/PABA, leveraging on prior connections between ProbLog and argumentation. Further, the connections pave the way towards novel forms of argumentative explanations for ProbLog's outputs.

In Enrico Pontelli, Stefania Costantini, Carmine Dodaro, Sarah Gaggl, Roberta Calegari, Artur D'Avila Garcez, Francesco Fabiano, Alessandra Mileo, Alessandra Russo and Francesca Toni: Proceedings 39th International Conference on Logic Programming (ICLP 2023), Imperial College London, UK, 9th July 2023 - 15th July 2023, Electronic Proceedings in Theoretical Computer Science 385, pp. 183–189.
Published: 12th September 2023.

ArXived at: https://dx.doi.org/10.4204/EPTCS.385.18 bibtex PDF
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