论文标题
在不确定性下进行顺序决策的因果解释
Causal Explanations for Sequential Decision Making Under Uncertainty
论文作者
论文摘要
我们介绍了一个新颖的框架，用于建立基于有因果推理的良好结构性因果模型范式建立的随机，顺序决策系统的因果解释。这个单个框架可以识别代理动作的多个，语义上不同的解释 - 以前无法做到这一点。在本文中，我们建立了使用此框架对马尔可夫决策过程的因果推断的精确方法和几种近似技术，然后取决于确切方法的适用性和某些运行时间边界。我们讨论了几种场景，以说明该框架的灵活性以及对人类受试者的实验结果，这些实验证实了这种方法的好处。
We introduce a novel framework for causal explanations of stochastic, sequential decision-making systems built on the well-studied structural causal model paradigm for causal reasoning. This single framework can identify multiple, semantically distinct explanations for agent actions -- something not previously possible. In this paper, we establish exact methods and several approximation techniques for causal inference on Markov decision processes using this framework, followed by results on the applicability of the exact methods and some run time bounds. We discuss several scenarios that illustrate the framework's flexibility and the results of experiments with human subjects that confirm the benefits of this approach.
