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

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 domain-specific PDDL bookmark predicates, increasing memory usage, and automated transformations to Positive Normal Form, further escalating memory consumption.