Lazy Evaluation of Negative Preconditions in Planning Domains (Extended Abstract)

Research output: Chapter in Book/Report/Conference proceedingConference contribution


AI planning technology faces performance issues with large-scale problems with negative preconditions. In this extended abstract, we show how to leverage the power of the Finite Domain Representation (FDR) used by the popular Fast Downward planner for such domains. FDR improves scalability thanks to its use of multi-valued state variables. However, it scales poorly when dealing with negative preconditions. We propose an alternative hybrid approach that evaluates negative preconditions on the fly during search but only when strictly needed. This is compared to the traditional use of PDDL bookmark predicates, which increases memory usage.
Original languageEnglish
Title of host publicationThe 17th International Symposium on Combinatorial Search (SoCS)
PublisherAAAI Press
Publication statusAccepted/In press - 10 Apr 2024

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