规则如何表示因果知识：用概率逻辑编程进行因果建模
How Rules Represent Causal Knowledge: Causal Modeling with Probabilistic Logic Programming
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中文总结 AI 辅助
研究如何将珀尔的因果方法引入概率逻辑编程，通过与不依赖时间概念的哲学基础对齐，提出形式因果语义等，使其语义与分层ProbLog程序的P-log语义一致，解决因果知识在不同形式主义中的应用问题。
中文摘要 AI 辅助
珀尔著名地论证了因果知识能够预测干预效果。相比之下，纯描述性知识仅支持从观察得出的结论。然而，他的因果理论仅在贝叶斯网络和因果模型中发展。因此，它很大程度上局限于无环因果关系，将其思想转移到其他形式主义有被误解或不一致的风险。本文将珀尔的因果方法引入概率逻辑编程（PLP）。为此，此类程序与先前工作中建立的不依赖时间概念的哲学基础保持一致，即假设所有相关事件同时发生。提出了这些程序的形式因果语义，以及干预概念和一种实现方式。结果表明，这种语义与分层ProbLog程序的P-log语义一致，而在非分层情况和其他PLP形式主义中两者可能不同。
英文摘要
Pearl famously argues that causal knowledge enables the prediction of intervention effects. By contrast, purely descriptive knowledge supports only conclusions drawn from observations. His theory of causality, however, is developed exclusively within Bayesian networks and causal models. Consequently, it is largely restricted to acyclic causal relationships, and transferring its ideas to other formalisms risks misinterpretation or inconsistency. This paper brings Pearl's approach to causality into probabilistic logic programming (PLP). To this end, such programs are aligned with philosophical foundations established in prior work that do not rely on temporal notions; that is, all relevant events are assumed to occur simultaneously. A formal causal semantics for these programs, together with a notion of intervention and an implementation, is proposed. It is shown that this semantics coincides with the P-log semantics for stratified ProbLog programs, while the two may differ in the non-stratified case and for other PLP formalisms.
