解决概率逻辑程序中因果顺序不确定的问题
From probability to causality in probabilistic logic programming
- 基于无环程序与贝叶斯网络关系推导因果唯一性条件
- 发现仅凭概率分布无法确定因果顺序，需额外约束
- 适用于需可靠干预推理的因果建模场景
概率逻辑编程是统计关系人工智能的一种形式化方法，支持来自系统外部的因果查询（如干预）。然而，当从数据中学习概率逻辑程序结构时，仅使用概率信息，单一概率分布可能对应多个因果顺序，导致干预推理模糊。本文利用无环概率逻辑程序与贝叶斯网络的关系，推导出概率信息能唯一确定因果顺序的条件。同时，通过考虑由底层关系词汇诱导的预设因果对称性，引入关系结构约束。最终提出一种验证已学习概率逻辑程序是否具备明确干预语义的方法。
原文摘要 · Abstract (English)
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.
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