AI要预测干预效果，必须理解因果原理而非仅知相关性。
How Artificial Intelligence Leads to Knowledge Why: An Inquiry Inspired by Aristotle's Posterior Analytics
- 用因果系统框架解析AI中的知识类型
- 证明干预预测需依赖‘为何’知识而非‘是什么’
- 为因果推理在AI中的应用提供理论依据
贝叶斯网络和因果模型为处理外部干预与反事实问题提供了框架，使任务超越了概率分布的局限。尽管这些形式化常被非正式地视为捕捉了因果知识，但缺乏对预测干预效果所需知识类型的正式理论界定。本文引入因果系统理论，阐明亚里士多德关于‘知其然’与‘知其所以然’的区分在人工智能中的意义。通过将现有AI技术解释为因果系统，探讨其对应的知识类型。进一步论证：只有具备‘为何’知识，才能实现对外部干预效果的准确预测，从而更精确地定义此类任务所需的知识本质。
原文摘要 · Abstract (English)
Bayesian networks and causal models provide frameworks for handling queries about external interventions and counterfactuals, enabling tasks that go beyond what probability distributions alone can address. While these formalisms are often informally described as capturing causal knowledge, there is a lack of a formal theory characterizing the type of knowledge required to predict the effects of external interventions. This work introduces the theoretical framework of causal systems to clarify Aristotle's distinction between knowledge that and knowledge why within artificial intelligence. By interpreting existing artificial intelligence technologies as causal systems, it investigates the corresponding types of knowledge. Furthermore, it argues that predicting the effects of external interventions is feasible only with knowledge why, providing a more precise understanding of the knowledge necessary for such tasks.
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