# Symbolic Parameter Learning in Probabilistic Answer Set Programming

arXiv: https://arxiv.org/abs/2408.08732 · HTML: https://mlsanity.com/paper/2408.08732/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 16 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [Efficient Knowledge Compilation Beyond Weighted Model Counting](https://mlsanity.com/paper/2205.07496/) (2022)
- [smProbLog: Stable Model Semantics in ProbLog for Probabilistic Argumentation](https://mlsanity.com/paper/2304.00879/) (2023)
- [dPASP: A Comprehensive Differentiable Probabilistic Answer Set Programming Environment For Neurosymbolic Learning and Reasoning](https://mlsanity.com/paper/2308.02944/) (2023)
- [Optimizing Probabilities in Probabilistic Logic Programs](https://mlsanity.com/paper/2108.03095/) (2021)

## Abstract (arXiv)

Parameter learning is a crucial task in the field of Statistical Relational Artificial Intelligence: given a probabilistic logic program and a set of observations in the form of interpretations, the goal is to learn the probabilities of the facts in the program such that the probabilities of the interpretations are maximized. In this paper, we propose two algorithms to solve such a task within the formalism of Probabilistic Answer Set Programming, both based on the extraction of symbolic equations representing the probabilities of the interpretations. The first solves the task using an off-the-shelf constrained optimization solver while the second is based on an implementation of the Expectation Maximization algorithm. Empirical results show that our proposals often outperform existing approaches based on projected answer set enumeration in terms of quality of the solution and in terms of execution time. The paper has been accepted at the ICLP2024 conference and is under consideration in Theory and Practice of Logic Programming (TPLP).