Explaining recommendations generated by MDPs

There has been little work in explaining recommendations generated by Markov Decision Processes (MDPs). We analyze the difculty of explaining policies computed automatically and identify a set of templates that can be used to generate explanations automatically at run-time. These templates are domain-independent and can be used in any application of an MDP. We show that no additional e ort is required from the MDP designer for producing such explanations. We use the problem of advising undergraduate students in their course selection to explain the recommendation for selecting speci c courses to students. We also propose an extension to leverage domain-speci c constructs using ontologies so that explanations can be made more user-friendly.