Treffer: Most Probable Explanation in Probabilistic Answer Set Programming
Title:
Most Probable Explanation in Probabilistic Answer Set Programming
Authors:
Contributors:
Azzolini, D., Mazzotta, G., Ricca, F., Riguzzi, F.
Publisher Information:
International Joint Conferences on Artificial Intelligence
USA
USA
Publication Year:
2025
Collection:
Università degli Studi di Ferrara: CINECA IRIS
Subject Terms:
Document Type:
Konferenz
conference object
File Description:
STAMPA
Language:
English
Relation:
info:eu-repo/semantics/altIdentifier/isbn/9781956792065; info:eu-repo/semantics/altIdentifier/wos/WOS:001634925300364; ispartofbook:IJCAI International Joint Conference on Artificial Intelligence; 34th Internationa Joint Conference on Artificial Intelligence, IJCAI 2025; firstpage:9049; lastpage:9057; numberofpages:9; https://hdl.handle.net/11392/2607150; https://www.ijcai.org/proceedings/2025/1006.pdf
DOI:
10.24963/ijcai.2025/1006
Availability:
Rights:
info:eu-repo/semantics/openAccess ; license:Copyright dell'editore ; license uri:publisher
Accession Number:
edsbas.8A3650F9
Database:
BASE
Weitere Informationen
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.