Weighted Rules under the Stable Model Semantics
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866915999811371008 |
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| author | Lee, Joohyung Wang, Yi |
| author_facet | Lee, Joohyung Wang, Yi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_09519 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Weighted Rules under the Stable Model Semantics Lee, Joohyung Wang, Yi Artificial Intelligence Logic in Computer Science 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. |
| title | Weighted Rules under the Stable Model Semantics |
| topic | Artificial Intelligence Logic in Computer Science |
| url | https://arxiv.org/abs/2605.09519 |