论文标题
马尔可夫决策过程中概率提高因果关系的基础
Foundations of probability-raising causality in Markov decision processes
论文作者
论文摘要
这项工作使用概率提出的原理引入了马尔可夫决策过程中的新颖原因关系。最初，考虑了一组状态作为原因和效果，随后将其扩展到常规路径属性作为效果，然后将其作为原因。本文奠定了数学基础，并分析了这些因果关系的算法。这包括用于检查原因条件的算法，并确定概率提高原因的存在。由于定义允许次优覆盖范围，研究了受统计分析概念启发的原因的质量度量。这些包括召回，覆盖率和F得分。分析了寻找有关这些措施的最佳原因的计算复杂性。
This work introduces a novel cause-effect relation in Markov decision processes using the probability-raising principle. Initially, sets of states as causes and effects are considered, which is subsequently extended to regular path properties as effects and then as causes. The paper lays the mathematical foundations and analyzes the algorithmic properties of these cause-effect relations. This includes algorithms for checking cause conditions given an effect and deciding the existence of probability-raising causes. As the definition allows for sub-optimal coverage properties, quality measures for causes inspired by concepts of statistical analysis are studied. These include recall, coverage ratio and f-score. The computational complexity for finding optimal causes with respect to these measures is analyzed.
