Coalgebraic Semantics for Probabilistic Logic Programming

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 probabili...

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Bibliographic Details
Published in:	Logical methods in computer science Vol. 17, Issue 2
Main Authors:	Gu, Tao, Zanasi, Fabio
Format:	Journal Article
Language:	English
Published:	Logical Methods in Computer Science e.V 01.01.2021
Subjects:	computer science - logic in computer science
ISSN:	1860-5974, 1860-5974
Online Access:	Get full text
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Summary:	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.
ISSN:	1860-5974 1860-5974
DOI:	10.23638/LMCS-17(2:2)2021