Optimizing Probabilities in Probabilistic Logic Programs. Issue 5 (23rd September 2021)

• Record Type: Journal Article
• Authors: AZZOLINI, DAMIANO; RIGUZZI, FABRIZIO
• Is Part Of: Theory and practice of logic programming. Volume 21:Issue 5(2021)
• Page Start: 543
• Page End: 556
• Publication Date: 2021-09-23
• Subjects: probabilistic logic programming -- optimization -- constraints; Logic programming -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; Constraint programming (Computer science) -- Periodicals; 005.115
• DOI: 10.1017/S1471068421000260
• Languages: English
• ISSNs: 1471-0684

Abstract

Abstract: 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.