Weighted Rules Under Stable Model Semantics Introduced
A new paper introduces weighted rules under the stable model semantics, drawing on log-linear models from Markov Logic. This approach addresses the deterministic nature of stable model semantics by enabling inconsistency resolution in answer set programs, ranking of stable models, probability assignment, and statistical inference for computing weighted stable models. The work includes formal comparisons with answer set programs, Markov Logic, ProbLog, and P-log. The paper is available on arXiv.
Key facts
- Weighted rules under stable model semantics are introduced.
- The method is based on log-linear models of Markov Logic.
- It resolves inconsistencies in answer set programs.
- It ranks stable models and associates probabilities.
- Statistical inference is applied to compute weighted stable models.
- Formal comparisons with answer set programs, Markov Logic, ProbLog, and P-log are presented.
- The paper is on arXiv under Computer Science > Artificial Intelligence.
Entities
Institutions
- arXiv