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 MDPs 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. These explanations can be generated automatically at run-time with no additional effort required from the MDP designer. We demonstrate this 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 these automatically generated explanations for course-advising through a user study involving students.
A Planning System Based on Markov Decision Processes to Guide People With Dementia Through Activities of Daily Living