Explainable Planning
This work tackles the problem of explainability in AI planning for users needing to interact with and trust intelligent systems, but it appears incremental as it builds on existing model-based approaches.
The paper addresses the challenge of making AI planning algorithms understandable to humans to support cooperation and build trust, proposing to leverage model-based representations as a familiar communication basis.
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.