The distribution semantics in probabilistic logic programming and probabilistic description logics: a survey

IF 1.9 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Intelligenza Artificiale Pub Date : 2023-06-07 DOI:10.3233/IA-221072
Elena Bellodi
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Abstract

Representing uncertain information is crucial for modeling real world domains. This has been fully recognized both in the field of Logic Programming and of Description Logics (DLs), with the introduction of probabilistic logic languages and various probabilistic extensions of DLs respectively. Several works have considered the distribution semantics as the underlying semantics of Probabilistic Logic Programming (PLP) languages and probabilistic DLs (PDLs), and have then targeted the problem of reasoning and learning in them. This paper is a survey of inference, parameter and structure learning algorithms for PLP languages and PDLs based on the distribution semantics. A few of these algorithms are also available as web applications.
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概率逻辑规划中的分布语义与概率描述逻辑综述
表示不确定信息对于建模真实世界领域至关重要。随着概率逻辑语言的引入和描述逻辑的各种概率扩展,这一点在逻辑规划领域和描述逻辑领域都得到了充分的认识。一些著作将分布语义视为概率逻辑规划(PLP)语言和概率dl (pdl)语言的底层语义,并针对其中的推理和学习问题进行了研究。本文综述了基于分布语义的PLP语言和pdl的推理、参数和结构学习算法。其中一些算法也可以作为web应用程序使用。
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来源期刊
Intelligenza Artificiale
Intelligenza Artificiale COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
3.50
自引率
6.70%
发文量
13
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