The Emerging Landscape of Explainable AI Planning and Decision Making
This is an incremental survey paper aimed at guiding new and established researchers in automated planning towards understanding explanations in human-in-the-loop systems.
The paper provides a comprehensive survey of Explainable AI Planning (XAIP), outlining recent work and contrasting it with earlier efforts in techniques, target users, and delivery mechanisms.
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.