Weighted Rules under the Stable Model Semantics
This academic paper introduces the concept of weighted rules within the stable model semantics, drawing inspiration from the log-linear models used in Markov Logic. The primary objective is to address the inherent deterministic limitations of traditional stable model semantics. By incorporating weights, the proposed framework offers versatile methods for resolving inconsistencies in answer set programs, ranking stable models, and associating probabilities with them. Furthermore, it enables the application of statistical inference to compute weighted stable models. The authors also provide formal comparisons with related logical formalisms, including standard answer set programs, Markov Logic, ProbLog, and P-log, to establish the theoretical positioning and advantages of their approach. This research contributes to the fields of Artificial Intelligence and Logic in Computer Science by enhancing the flexibility and applicability of logical reasoning systems. The work was submitted to arXiv in May 2026 and references prior publication in the Proceedings of the 15th International Conference on Principles of Knowledge Representation and Reasoning (KR 2016).
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Weighted Rules under the Stable Model Semantics
This academic paper introduces the concept of weighted rules within the stable model semantics, drawing inspiration from the log-linear models used in Markov Logic. The primary objective is to address the inherent deterministic limitations of traditional stable model semantics. By incorporating weights, the proposed framework offers versatile methods for resolving inconsistencies in answer set programs, ranking stable models, and associating probabilities with them. Furthermore, it enables the application of statistical inference to compute weighted stable models. The authors also provide formal comparisons with related logical formalisms, including standard answer set programs, Markov Logic, ProbLog, and P-log, to establish the theoretical positioning and advantages of their approach. This research contributes to the fields of Artificial Intelligence and Logic in Computer Science by enhancing the flexibility and applicability of logical reasoning systems. The work was submitted to arXiv in May 2026 and references prior publication in the Proceedings of the 15th International Conference on Principles of Knowledge Representation and Reasoning (KR 2016).
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