Interactive Exploration of Plan Spaces

Many planning applications require not only a single solution but benefit substantially from having a set of possible plans from which users can select, for example, when explaining plans. For decades, research in classical AI planning has primarily focused on quickly finding single plans. Only recently researchers have started to investigate preferences, enumerate plans by top-k planning, or count plans to reason about the plan space. Unfortunately, reasoning about the plan space is computationally extremely hard and feeding many similar plans to the user is hardly practical. To circumvent computational shortcomings while still being able to reason about variability in plans, faceted actions have been introduced very recently. These are meaningful actions that can be used by some plan but are not required by all plans. Enforcing or forbidding such facets allows for navigating even large plan spaces while ensuring desired properties quickly and step by step. In this paper, we illustrate an industrial challenge, the Beluga logistics problem of Airbus, where reasoning with facets enables targeted plan space navigation. We present an approach to handle large plan spaces iteratively and interactively and present a tool that we call PlanPilot.