Expectation-Aware Planning: A Unifying Framework for Synthesizing and
  Executing Self-Explaining Plans for Human-Aware Planning
  In this work, we present a new planning formalism called Expectation-Aware
planning for decision making with humans in the loop where the human's
expectations about an agent may differ from the agent's own model. We show how
this formulation allows agents to not only leverage existing strategies for
handling model differences but can also exhibit novel behaviors that are
generated through the combination of these different strategies. Our
formulation also reveals a deep connection to existing approaches in epistemic
planning. Specifically, we show how we can leverage classical planning
compilations for epistemic planning to solve Expectation-Aware planning
problems. To the best of our knowledge, the proposed formulation is the first
complete solution to decision-making in the presence of diverging user
expectations that is amenable to a classical planning compilation while
successfully combining previous works on explanation and explicability. We
empirically show how our approach provides a computational advantage over
existing approximate approaches that unnecessarily try to search in the space
of models while also failing to facilitate the full gamut of behaviors enabled
by our framework.
