Proceedings of the Twenty-Second International Conference on Automated Planning and Scheduling Making Hybrid Plans More Clear to Human Users — A Formal Approach for Generating Sound Explanations

Citations in the corpus

• Explainable Planning (2017, methodology): “…planner outputs (e.g., PDDL plans) in forms that humans can easily understand; the design of interfaces that help this understanding (e.g., spoken language dialog systems [Bidot et al., 2010]); and the description of causal and temporal relations for plan steps (e.g., [Seegebarth et al., 2012]).”
• Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation (2018, background): “In the domain of planning, research in plan explanations attempts to make the systems’ output more understandable through a more understandable representation of plans [11] and by generating explanations using natural language [12].”
• Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior Explanations (2018, result): “Earlier works like [Seegebarth et al., 2012; Bercher et al., 2014; Kambhampati, 1990] looked at explanations as a way of describing the effects of plans, while works like [Sohrabi, Baier, and McIlraith, 2011; Meadows, Langley, and Emery, 2013] looked at plans itself as explanations for a set of…”