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.

翻译：我们研究了带有定性状态轨迹约束的PDDL片段中的规划问题，这些约束捕获了现实世界问题中常见的安全性要求、任务排序条件和中间子目标。处理此类问题的一个主要方法是通过编译消除约束，从而生成一个受先进规划器支持的问题。然而，现有的编译器在处理具有大量对象和高元数动作的问题时无法有效扩展，因为它们需要在编译前对问题进行实例化。为解决这一问题，我们提出了两种无需实例化即可编译消除约束的方法，使其适用于大规模规划问题。我们证明了编译器的正确性，并概述了其最坏情况下的时间复杂度。此外，我们在最新国际规划竞赛使用的领域中进行了可复现的实证评估。结果表明，我们的方法高效且生成的规划规范比基于领域实例化的编译器生成的规范简洁数个数量级，同时在使用先进规划器进行规划时仍保持竞争力。