The representation of scenarios drawn from real-world domains is certainly favored by the presence of simple but powerful languages capable of capturing all facets of the problem. Probabilistic Logic Programming (PLP) [5,11] plays a fundamental role in this thanks to its ability to represent uncertain and complex information [3,10] and the possibility to be integrated in sub-symbolic systems to help the explainability of the models [9]. Several probabilistic logic programming languages based on the so-called distribution semantics [14] have been proposed over the years, among which are PRISM [14], Logic Programs with Annotated Disjunctions (LPADs) [15], and ProbLog [6].

Abduction in Probabilistic Logic Programs

Azzolini D.;Bellodi E.;Riguzzi F.;Zese R.
2022

Abstract

The representation of scenarios drawn from real-world domains is certainly favored by the presence of simple but powerful languages capable of capturing all facets of the problem. Probabilistic Logic Programming (PLP) [5,11] plays a fundamental role in this thanks to its ability to represent uncertain and complex information [3,10] and the possibility to be integrated in sub-symbolic systems to help the explainability of the models [9]. Several probabilistic logic programming languages based on the so-called distribution semantics [14] have been proposed over the years, among which are PRISM [14], Logic Programs with Annotated Disjunctions (LPADs) [15], and ProbLog [6].
2022
Abduction, Probabilistic Logic Programming
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2494034
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