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 inTheory and practice of logic programming Vol. 21; no. 5; pp. 543 - 556
Main Authors AZZOLINI, DAMIANO, RIGUZZI, FABRIZIO
Format Journal Article
LanguageEnglish
Published Cambridge, UK Cambridge University Press 01.09.2021
Subjects
Algorithms
Logic programming
Logic programs
Optimization
Original Article
Random variables
Statistical analysis
optimization
constraints
probabilistic logic programming
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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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1471-0684
1475-3081
DOI:10.1017/S1471068421000260