将概率事件演算转化为马尔可夫决策过程，实现可解释的智能体规划。
A Translation of Probabilistic Event Calculus into Markov Decision Processes
- 通过引入动作发生情境，将概率事件演算映射为马尔可夫决策过程。
- 支持时间推理与目标驱动规划，且可将学习策略转回可读的逻辑表达。
- 适合需要可解释性与自动规划结合的研究者使用。
概率事件演算（PEC）是一种用于不确定环境中动作与效应推理的逻辑框架，能够表示概率性叙事并计算时间投影。该形式化在叙事推理中具有高度可解释性与表达力，但缺乏目标导向推理机制。本文通过构建PEC领域到马尔可夫决策过程（MDP）的正式转换，提出“动作发生情境”概念以保留PEC灵活的动作语义。由此产生的PEC-MDP形式化使大量为MDP开发的算法与理论工具可应用于可解释的叙事领域。我们展示了该转换如何支持时间推理任务与目标驱动规划，并提供将学习策略映射回人类可读的PEC表示的方法，在保持可解释性的同时扩展了PEC的能力。
原文摘要 · Abstract (English)
Probabilistic Event Calculus (PEC) is a logical framework for reasoning about actions and their effects in uncertain environments, which enables the representation of probabilistic narratives and computation of temporal projections. The PEC formalism offers significant advantages in interpretability and expressiveness for narrative reasoning. However, it lacks mechanisms for goal-directed reasoning. This paper bridges this gap by developing a formal translation of PEC domains into Markov Decision Processes (MDPs), introducing the concept of "action-taking situations" to preserve PEC's flexible action semantics. The resulting PEC-MDP formalism enables the extensive collection of algorithms and theoretical tools developed for MDPs to be applied to PEC's interpretable narrative domains. We demonstrate how the translation supports both temporal reasoning tasks and objective-driven planning, with methods for mapping learned policies back into human-readable PEC representations, maintaining interpretability while extending PEC's capabilities.
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