优化互补式模式数据库构建，提升规划求解性能。
Refinements on the Complementary PDB Construction Mechanism
- 改进模式生成策略，增强不同数据库间的互补性。
- 在IPC 2018基准上，新版本规划器超越原版，表现更优。
- 适合对启发式搜索与自动启发式生成感兴趣的读者。
模式数据库（PDB）是主流的自动化启发式生成技术之一，通过选取部分变量将状态映射到抽象状态，并在查表中存储其到抽象目标的最优代价。近年来符号搜索的发展使基于符号化PDB的规划器在2018年国际规划竞赛（IPC 2018）中取得显著成果。其中，互补1（CPC1）在成本最优赛道中并列第二，是最佳非组合型规划器，仅落后于冠军2个任务。其采用多种模式生成算法构造互补的PDB。赛后实验表明仍有优化空间。本文针对CPC1的PDB构建机制进行改进，基于IPC 2018基准测试，结果表明修改后的规划器相较原版有显著提升。
原文摘要 · Abstract (English)
Pattern database (PDB) is one of the most popular automated heuristic generation techniques. A PDB maps states in a planning task to abstract states by considering a subset of variables and stores their optimal costs to the abstract goal in a look up table. As the result of the progress made on symbolic search over recent years, symbolic-PDB-based planners achieved impressive results in the International Planning Competition (IPC) 2018. Among them, Complementary 1 (CPC1) tied as the second best planners and the best non-portfolio planners in the cost optimal track, only 2 tasks behind the winner. It uses a combination of different pattern generation algorithms to construct PDBs that are complementary to existing ones. As shown in the post contest experiments, there is room for improvement. In this paper, we would like to present our work on refining the PDB construction mechanism of CPC1. By testing on IPC 2018 benchmarks, the results show that a significant improvement is made on our modified planner over the original version.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。