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arXiv · 2012.03916

Coalgebraic Semantics for Probabilistic Logic Programming

Tao Gu·Fabio Zanasi
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Abstract

Probabilistic logic programming is increasingly important in artificial intelligence and related fields as a formalism to reason about uncertainty. It generalises logic programming with the possibility of annotating clauses with probabilities. This paper proposes a coalgebraic semantics on probabilistic logic programming. Programs are modelled as coalgebras for a certain functor F, and two semantics are given in terms of cofree coalgebras. First, the F-coalgebra yields a semantics in terms of derivation trees. Second, by embedding F into another type G, as cofree G-coalgebra we obtain a `possible worlds' interpretation of programs, from which one may recover the usual distribution semantics of probabilistic logic programming. Furthermore, we show that a similar approach can be used to provide a coalgebraic semantics to weighted logic programming.

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Tao Gu, Fabio Zanasi. 2020-12-07. Coalgebraic Semantics for Probabilistic Logic Programming. https://doi.org/10.23638/lmcs-17(2%3A2)2021

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