Interactive Exploration of Plan Spaces

Daniel GnadDaniel Gnad, Markus HecherMarkus Hecher, Sarah Alice GagglSarah Alice Gaggl, Dominik RusovacDominik Rusovac, David Jakob SpeckDavid Jakob Speck, Johannes K. FichteJohannes K. Fichte

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Daniel Gnad, Markus Hecher, Sarah Alice Gaggl, Dominik Rusovac, David Jakob Speck, Johannes K. Fichte
Interactive Exploration of Plan Spaces
KR in the Wild track at KR 2025, to appear

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KurzfassungAbstract
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 present a tool that we call planpilot.
Forschungsgruppe:Research Group: Logische Programmierung und ArgumentationLogic Programming and Argumentation

@inproceedings{GHGRSF2025,
  author    = {Daniel Gnad and Markus Hecher and Sarah Alice Gaggl and Dominik
               Rusovac and David Jakob Speck and Johannes K. Fichte},
  title     = {Interactive Exploration of Plan Spaces},
  booktitle = {KR in the Wild track at {KR} 2025},
  year      = {2025}
}