Optimizing Probabilities in Probabilistic Logic Programs

TL;DR

This paper introduces Probabilistic Optimizable Logic Programs and an algorithm to optimize probability assignments under constraints, enhancing probabilistic logic programming for uncertain problem modeling.

Contribution

It presents a new class of probabilistic logic programs and an algorithm for probability optimization under constraints, advancing the field's capability to handle uncertainty.

Findings

Effective algorithm for probability assignment optimization

Supports constraint satisfaction and objective maximization

Enhances probabilistic logic programming applications

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.