Towards Exploiting Generic Problem Structures in Explanations for Automated Planning

Explainable AI is becoming an area of key focus in Artificial Intelligence. Within Automated Planning (AP) the area Explainable Planning (XAIP) focuses on explanations of the planning process. The relative transparency and flexibility of the planning process have been identified as key aspects suggesting that AP is well positioned to make an important contribution in Explainable AI [8]. However, there is still a wide gap between explanations that can be directly extracted from AP models and effective explanations. There are a growing number of frameworks that are considering the problem from both the user side, where it is interesting to understand the form that an explanation might take; as well as the planner side, which must be able to explain various decisions and related properties. However, approaches have focused on single domain settings, where substantial domain specific content is produced, or at a general level, where only abstract planning concepts can be used. We aim to develop an abstraction layer that sits between these and exploits the often overlapping concepts and structures that exist between many planning domains. We propose exploiting domain analysis techniques in order to identify common roles and generic problem structures (GPSs). By attaching the concepts used for explanation to these structures we can exploit the contextual information supported by the structure, and also reduce the burden of constructing explanations in domains where these structures exist. In this work we explore the opportunities for exploiting GPSs in XAIP.