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

## TL;DR

This paper introduces Expectation-Aware planning, a formalism enabling agents to handle differing human expectations by synthesizing self-explaining plans, leveraging classical epistemic planning techniques, and demonstrating computational advantages over existing methods.

## Contribution

It presents the first complete, classical planning-compatible framework for decision-making with diverging human expectations, integrating explanation and explicability strategies.

## Key findings

- Provides a unified formalism for human-aware planning
- Leverages classical epistemic planning for solution synthesis
- Shows computational efficiency over approximate methods

## Abstract

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.