Tao Gu ; Fabio Zanasi - Coalgebraic Semantics for Probabilistic Logic Programming

lmcs:6967 - Logical Methods in Computer Science, April 12, 2021, Volume 17, Issue 2 - https://doi.org/10.23638/LMCS-17(2:2)2021
Coalgebraic Semantics for Probabilistic Logic Programming

Authors: Tao Gu ; Fabio Zanasi

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.

Volume: Volume 17, Issue 2
Published on: April 12, 2021
Accepted on: December 10, 2020
Submitted on: December 10, 2020
Keywords: Computer Science - Logic in Computer Science


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