Minimal Sufficient Explanations for Factored Markov Decision Processes
Minimal Sufficient Explanations for Factored Markov Decision Processes
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DOI：
10.1609/icaps.v19i1.13365
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发表时间：
2009-09
期刊：
影响因子：
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通讯作者：
O. Khan;P. Poupart;J. Black
中科院分区：
文献类型：
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作者：
O. Khan;P. Poupart;J. Black
马尔可夫决策过程（MDP）的政策解释是复杂的，由于其概率和顺序的性质。我们提出了一种技术来解释政策的因素MDP填充一组域独立的模板。我们还提出了一种机制，以确定一个最小的模板集，一起看，完全证明了政策。我们的解释可以在运行时自动生成，而不需要MDP设计人员进行额外的工作。我们展示了我们的技术使用的问题，建议本科生在他们的课程选择和协助痴呆症患者完成洗手的任务。我们还通过涉及学生的用户研究来评估我们对课程建议的解释。
Explaining policies of Markov Decision Processes (MDPs) is complicated due to their probabilistic and sequential nature. We present a technique to explain policies for factored MDP by populating a set of domain-independent templates. We also present a mechanism to determine a minimal set of templates that, viewed together, completely justify the policy. Our explanations can be generated automatically at run-time with no additional effort required from the MDP designer. We demonstrate our technique using the problems of advising undergraduate students in their course selection and assisting people with dementia in completing the task of handwashing. We also evaluate our explanations for course-advising through a user study involving students.