研究不确定环境中的真实因果关系，让智能体能更合理地判断行为原因。
Reasoning about Actual Causes in Nondeterministic Domains -- Extended Version
- 提出'必然因果'和'可能因果'概念，刻画不确定环境下行为的真实影响。
- 扩展情境演算的回溯推理，支持在非确定环境中推断实际因果。
- 适合研究智能体决策、因果推理或形式化理性的研究人员。
理解观察背后的因果关系对形式化理性至关重要。尽管已有大量关于根本原因分析的研究，但多数工作集中在确定性场景。本文研究更贴近现实的非确定性领域，其中智能体无法控制或了解环境所作的选择。我们基于近期在非确定性情境演算中关于真实因果的初步工作，形式化了更复杂的实际因果推理方法。提出了'必然因果'与'可能因果'的概念，以表示此类领域中智能体行为的实际因果作用。随后，展示了如何将情境演算中的回溯推理扩展至支持这些因果概念的推理。
原文摘要 · Abstract (English)
Reasoning about the causes behind observations is crucial to the formalization of rationality. While extensive research has been conducted on root cause analysis, most studies have predominantly focused on deterministic settings. In this paper, we investigate causation in more realistic nondeterministic domains, where the agent does not have any control on and may not know the choices that are made by the environment. We build on recent preliminary work on actual causation in the nondeterministic situation calculus to formalize more sophisticated forms of reasoning about actual causes in such domains. We investigate the notions of ``Certainly Causes'' and ``Possibly Causes'' that enable the representation of actual cause for agent actions in these domains. We then show how regression in the situation calculus can be extended to reason about such notions of actual causes.
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