Recent advances in planning have explored using learning methods to help planning. However, little attention has been given to adapting search algorithms to work better with learning systems. In this paper, we introduce partial-space search, a new search space for classical planning that leverages the relational structure of actions given by PDDL action schemas -- a structure overlooked by traditional planning approaches. Partial-space search provides a more granular view of the search space and allows earlier pruning of poor actions compared to state-space search. To guide partial-space search, we introduce action set heuristics that evaluate sets of actions in a state. We describe how to automatically convert existing heuristics into action set heuristics. We also train action set heuristics from scratch using large training datasets from partial-space search. Our new planner, LazyLifted, exploits our better integrated search and learning heuristics and outperforms the state-of-the-art ML-based heuristic on IPC 2023 learning track (LT) benchmarks. We also show the efficiency of LazyLifted on high-branching factor tasks and show that it surpasses LAMA in the combined IPC 2023 LT and high-branching factor benchmarks.

本研究解决了现有规划方法在与学习系统结合时搜索算法适应性不足的问题。提出了一种新型的部分空间搜索，通过利用PDDL行动模式中的动作关系结构，提供了更细粒度的搜索视图，并能更早剔除不良动作。实验结果表明，新规划器LazyLifted在最新基准测试中表现优于现有的机器学习启发式算法，尤其在高分支因子任务中展现出高效性。

利用行动关系结构进行综合学习与规划