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In this work, we present a new planning formalism called Expectation-Awareplanning for decision making with humans in the loop where the human'sexpectations about an agent may differ from the agent's own model. We show howthis formulation allows agents to not only leverage existing strategies forhandling model differences but can also exhibit novel behaviors that aregenerated through the combination of these different strategies. Ourformulation also reveals a deep connection to existing approaches in epistemicplanning. Specifically, we show how we can leverage classical planningcompilations for epistemic planning to solve Expectation-Aware planningproblems. To the best of our knowledge, the proposed formulation is the firstcomplete solution to decision-making in the presence of diverging userexpectations that is amenable to a classical planning compilation whilesuccessfully combining previous works on explanation and explicability. Weempirically show how our approach provides a computational advantage overexisting approximate approaches that unnecessarily try to search in the spaceof models while also failing to facilitate the full gamut of behaviors enabledby our framework.

Expectation-Aware Planning: A Unifying Framework for Synthesizing and Executing Self-Explaining Plans for Human-Aware Planning