Optimizing Probabilities in Probabilistic Logic Programs

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. This paper is under consideration for acceptance in Theory and Practice of Logic Programming.

Paper presented at the 37th International Conference on Logic Programming (ICLP 2021), 16 pages

References in corpus (4)

• A Knowledge Compilation Map
• Parameter Learning of Logic Programs for Symbolic-Statistical Modeling
• Inference and learning in probabilistic logic programs using weighted Boolean formulas
• Stochastic Constraint Programming