发表机构
Ludwig-Maximilians-Universität München; Eberhard-Karls-Universität Tübingen; German University of Digital Science（慕尼黑大学; 蒂宾根大学; 德国数字科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对概率逻辑程序从数据学习时因果顺序不唯一的问题，结合贝叶斯网络关系推导条件并纳入因果对称约束，提出可验证干预语义的方法。
AI 中文摘要
概率逻辑编程是一种支持因果查询（包括系统外部干预）的统计关系人工智能形式体系。然而，当从数据中学习概率逻辑程序的结构时，仅使用概率信息，且单一概率分布可能与多个因果顺序兼容，这导致干预推理存在歧义，进而提出了分布何时能唯一确定因果顺序的问题。利用无环概率逻辑程序与贝叶斯网络之间的关系，我们推导了程序中编码的概率信息确定唯一因果顺序的条件，同时考虑基础关系词汇诱导的规定因果对称集合，纳入关系结构产生的约束。最终得到一种方法，用于验证学习后的概率逻辑程序是否支持明确定义的干预语义。
英文摘要
Probabilistic logic programming is a formalism of statistical relational artificial intelligence that supports causal queries, including interventions from outside the system. When the structure of a probabilistic logic program is learned from data, however, only probabilistic information is used, and a single probability distribution may be compatible with several causal orders. This leads to ambiguity in interventional reasoning, raising the question of when the causal order is uniquely determined by the distribution. Exploiting the relationship between acyclic probabilistic logic programs and Bayesian networks, we derive conditions under which the probabilistic information encoded in a program determines a unique causal order. We also incorporate constraints arising from relational structure by taking into account prescribed sets of causal symmetries induced by the underlying relational vocabulary. The result is a method for verifying when a learned probabilistic logic program supports well-defined intervention semantics.
CommentsAccepted and presented at IJCLR 2025
