An MDP Approach for Explanation Generation

In order to assist a power plant operator to face unusual situations, we have developed an intelligent assistant that explains the suggested commands generated by an MDP-based planning system. This assistant provides the trainee a better understanding of the recommended actions to later generalize them to similar situations. In a first stage, built-in explanations are predefined by a domain expert and encapsulated within explanation units. When the operator takes an incorrect action, an explanation is automatically generated. A controlled user study in this stage showed that explanations have a positive impact on learning. In a second stage, we are developing an automatic explanation generation mechanism based on a factored representation of the decision model used by the planning system. As part of this stage, we describe an algorithm to select a relevant variable, which is a key component of the explanations defined by the expert.