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arXiv 2012.03916cs.LO
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概率逻辑编程的余代数语义

Coalgebraic Semantics for Probabilistic Logic Programming

Tao Gu, Fabio Zanasi

AI总结:

本文提出概率逻辑编程的余代数语义,通过函子F的余代数建模程序,并利用余自由余代数给出推导树与可能世界两种语义,可恢复分布语义,并扩展至加权逻辑编程。

摘要:

概率逻辑编程在人工智能及相关领域中作为推理不确定性的形式体系日益重要。它通过为子句标注概率的可能性来推广逻辑编程。本文提出了概率逻辑编程上的余代数语义。程序被建模为某个函子F的余代数,并基于余自由余代数给出了两种语义。首先,F-余代数产生基于推导树的语义。其次,通过将F嵌入到另一类型G中,作为余自由G-余代数,我们获得程序的“可能世界”解释,从中可以恢复概率逻辑编程的通常分布语义。此外,我们表明类似的方法可用于为加权逻辑编程提供余代数语义。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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