Two Constraint Compilation Methods for Lifted Planning
This work addresses scalability for large-scale planning problems with constraints, such as safety and task ordering, which is incremental as it builds on existing compilation approaches.
The authors tackled the scalability issue of existing compilers for planning with qualitative state-trajectory constraints, which require grounding and fail on large problems with many objects and high-arity actions. They proposed two constraint compilation methods that avoid grounding, resulting in planning specifications that are orders of magnitude more succinct and remain competitive with state-of-the-art planners.
We study planning in a fragment of PDDL with qualitative state-trajectory constraints, capturing safety requirements, task ordering conditions, and intermediate sub-goals commonly found in real-world problems. A prominent approach to tackle such problems is to compile their constraints away, leading to a problem that is supported by state-of-the-art planners. Unfortunately, existing compilers do not scale on problems with a large number of objects and high-arity actions, as they necessitate grounding the problem before compilation. To address this issue, we propose two methods for compiling away constraints without grounding, making them suitable for large-scale planning problems. We prove the correctness of our compilers and outline their worst-case time complexity. Moreover, we present a reproducible empirical evaluation on the domains used in the latest International Planning Competition. Our results demonstrate that our methods are efficient and produce planning specifications that are orders of magnitude more succinct than the ones produced by compilers that ground the domain, while remaining competitive when used for planning with a state-of-the-art planner.