Weighted Rules under the Stable Model Semantics
Artificial Intelligence
2026-05-12 v1 Logic in Computer Science
Abstract
We introduce the concept of weighted rules under the stable model semantics following the log-linear models of Markov Logic. This provides versatile methods to overcome the deterministic nature of the stable model semantics, such as resolving inconsistencies in answer set programs, ranking stable models, associating probability to stable models, and applying statistical inference to computing weighted stable models. We also present formal comparisons with related formalisms, such as answer set programs, Markov Logic, ProbLog, and P-log.
Cite
@article{arxiv.2605.09519,
title = {Weighted Rules under the Stable Model Semantics},
author = {Joohyung Lee and Yi Wang},
journal= {arXiv preprint arXiv:2605.09519},
year = {2026}
}