Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior
  Explanations
  There is a growing interest within the AI research community to develop
autonomous systems capable of explaining their behavior to users. One aspect of
the explanation generation problem that has yet to receive much attention is
the task of explaining plans to users whose level of expertise differ from that
of the explainer. We propose an approach for addressing this problem by
representing the user's model as an abstraction of the domain model that the
planner uses. We present algorithms for generating minimal explanations in
cases where this abstract human model is not known. We reduce the problem of
generating explanation to a search over the space of abstract models and
investigate possible greedy approximations for minimal explanations. We also
empirically show that our approach can efficiently compute explanations for a
variety of problems.
