The Emerging Landscape of Explainable AI Planning and Decision Making
arXiv:2002.11697
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.
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Cited by in corpus (6)
- Question-Driven Design Process for Explainable AI User Experiences
- Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience
- A Bayesian Account of Measures of Interpretability in Human-AI Interaction
- Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations
- Explainable Autonomous Robots: A Survey and Perspective
- Explainable AI for System Failures: Generating Explanations that Improve Human Assistance in Fault Recovery