---
title: "Most Probable Explanation in Probabilistic Answer Set Programming"
authors:
  - "Damiano Azzolini"
  - "Giuseppe Mazzotta"
  - "Francesco Ricca"
  - "Fabrizio Riguzzi"
doi: "10.24963/ijcai.2025/1006"
url: "https://ijcai.org/proceedings/2025/1006"
pdf: "https://ijcai.org/proceedings/2025/1006.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "9049-9057"
track: "Main Track"
keywords:
  - "Uncertainty in AI: UAI: Uncertainty representations"
  - "Uncertainty in AI: UAI: Statistical relational AI"
  - "Knowledge Representation and Reasoning: KRR: Learning and reasoning"
  - "Knowledge Representation and Reasoning: KRR: Non-monotonic reasoning"
---

# Most Probable Explanation in Probabilistic Answer Set Programming

Damiano Azzolini, Giuseppe Mazzotta, Francesco Ricca, Fabrizio Riguzzi

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 9049-9057. DOI: [10.24963/ijcai.2025/1006](https://doi.org/10.24963/ijcai.2025/1006)

## Abstract

Most Probable Explanation (MPE) is a fundamental problem in statistical relational artificial intelligence.
In the context of Probabilistic Answer Set Programming (PASP), solving MPE is still an open research problem.
In this paper, we present three novel approaches for solving the MPE task in PASP that are based on: i) Algebraic Model Counting, ii) Answer Set Programming (ASP), and iii) ASP with quantifiers (ASP(Q)).
These approaches are implemented and evaluated against existing solvers across different datasets and configurations.
Empirical results demonstrate that the novel solutions consistently outperform existing alternatives for non-stratified programs.