  In order to have effective human-AI collaboration, it is necessary to address
how the AI agent's behavior is being perceived by the humans-in-the-loop. When
the agent's task plans are generated without such considerations, they may
often demonstrate inexplicable behavior from the human's point of view. This
problem may arise due to the human's partial or inaccurate understanding of the
agent's planning model. This may have serious implications from increased
cognitive load to more serious concerns of safety around a physical agent. In
this paper, we address this issue by modeling plan explicability as a function
of the distance between a plan that agent makes and the plan that human expects
it to make. We learn a regression model for mapping the plan distances to
explicability scores of plans and develop an anytime search algorithm that can
use this model as a heuristic to come up with progressively explicable plans.
We evaluate the effectiveness of our approach in a simulated autonomous car
domain and a physical robot domain.
