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Explainable Planning
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As AI is increasingly being adopted into application solutions, the challenge of supporting interaction with humans is becoming more apparent. Partly this is to support integrated working styles, in which humans and intelligent systems cooperate in problem-solving, but also it is a necessary step in the process of building trust as humans migrate greater responsibility to such systems. The challenge is to find effective ways to communicate the foundations of AI-driven behaviour, when the algorithms that drive it are far from transparent to humans. In this paper we consider the opportunities that arise in AI planning, exploiting the model-based representations that form a familiar and common basis for communication with users, while acknowledging the gap between planning algorithms and human problem-solving.
Forward citations
Cited by 4 Pith papers
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Counterfactual Explanations as Plans
A formal account in modal situation calculus that defines counterfactual explanations as minimally distant alternative plans which toggle a goal, including reconciliation through added knowledge or corrected beliefs.
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Challenges in Human-Agent Communication
A position paper identifying and naming twelve communication challenges between humans and modern generative AI agents, grouped into three categories.
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Towards Explainable AI Planning as a Service
Explainable planning can be delivered as a service wrapper around a trusted planner, which answers contrastive questions by compiling them into constrained planning problems.
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A Survey of Explainable Reinforcement Learning: Targets, Methods and Needs
A survey of 250+ explainable-reinforcement-learning papers proposes a What/How taxonomy and reports that sequence-level explanations are rare (11 works) compared with policy-level (175) and action-level (89) ones.
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