# Optimizing Probabilities in Probabilistic Logic Programs

arXiv: https://arxiv.org/abs/2108.03095 · HTML: https://mlsanity.com/paper/2108.03095/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2021.

## Built on by (most cited first, 1 of 1)

- [Symbolic Parameter Learning in Probabilistic Answer Set Programming](https://mlsanity.com/paper/2408.08732/) (2024)

## Abstract (arXiv)

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.