Optimizing Probabilities in Probabilistic Logic Programs

Probabilistic Logic Programming is an effective formalism for encoding problems characterized by uncertainty. Some of these problems may require the optimization of probability values subject to constraints among probability distributions of random variables. Here, we introduce a new class of probab...

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Bibliographic Details
Published inarXiv.org
Main Authors Azzolini, Damiano, Riguzzi, Fabrizio
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 06.08.2021
Subjects
Algorithms
Computer Science - Logic in Computer Science
Logic programming
Logic programs
Optimization
Random variables
Statistical analysis
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Summary:Probabilistic Logic Programming is an effective formalism for encoding problems characterized by uncertainty. Some of these problems may require the optimization of probability values subject to constraints among probability distributions of random variables. Here, we introduce a new class of probabilistic logic programs, namely Probabilistic Optimizable Logic Programs, and we provide an effective algorithm to find the best assignment to probabilities of random variables, such that a set of constraints is satisfied and an objective function is optimized. This paper is under consideration for acceptance in Theory and Practice of Logic Programming.
Bibliography:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2331-8422
DOI:10.48550/arxiv.2108.03095