The Emerging Landscape of Explainable AI Planning and Decision Making

TL;DR

This paper surveys the recent developments in Explainable AI Planning (XAIP), highlighting techniques, user focus, and delivery methods, to guide researchers and understand the field's evolution.

Contribution

It offers a comprehensive overview of XAIP's emerging landscape, contrasting it with past efforts and emphasizing explanation roles in human-in-the-loop systems.

Findings

Identifies key techniques and approaches in XAIP

Highlights target users and explanation delivery mechanisms

Provides guidance for new and established researchers

Abstract

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.