  In this paper, we provide a comprehensive outline of the different threads of
work in Explainable AI Planning (XAIP) that has emerged as a focus area in the
last couple of years and contrast that with earlier efforts in the field in
terms of techniques, target users, and delivery mechanisms. We hope that the
survey will provide guidance to new researchers in automated planning towards
the role of explanations in the effective design of human-in-the-loop systems,
as well as provide the established researcher with some perspective on the
evolution of the exciting world of explainable planning.
