为稳定模型语义引入加权规则，实现概率推理与模型排序
Weighted Rules under the Stable Model Semantics
- 基于马尔可夫逻辑的对数线性模型，给规则赋予权重
- 可解决答案集程序中的不一致问题并生成加权稳定模型
- 适合需概率推理或模型排序的逻辑编程场景
我们引入了在稳定模型语义下的加权规则，借鉴马尔可夫逻辑的对数线性模型。该方法提供了灵活手段，克服稳定模型语义的确定性缺陷，包括解决答案集程序中的不一致性、对稳定模型进行排序、为稳定模型分配概率，以及通过统计推断计算加权稳定模型。我们还对相关形式化系统（如答案集程序、马尔可夫逻辑、ProbLog 和 P-log）进行了正式比较。
原文摘要 · Abstract (English)
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.
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