放弃顺序，保留层次：HTN规划的去序处理
Lose the Order, Keep the Hierarchy: Deordering HTN Plans
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中文总结 AI 辅助
本文将经典规划的两种去序技术适配HTN场景，在IPC 2023基准上与Optiplan对比，实现了HTN规划顺序约束的大幅减少，同时关键路径长度也有一定降低。
中文摘要 AI 辅助
层次任务网络（HTN）规划是一种基于任务分解的强大规划形式。尽管多数文献聚焦于规划生成，但对规划后优化的关注相对较少。尤其在经典规划中已被广泛研究的规划去序，在HTN场景中仍研究不足。规划去序会移除规划中动作间不必要的顺序约束，同时保持规划有效。本文将两种已有的经典规划去序技术进行适配，扩展这些技术以适配层次分解约束。我们在IPC 2023部分有序HTN基准上评估所提方法，并与直接生成部分有序规划的HTN规划器Optiplan对比。结果显示，两种实现中顺序约束的数量均大幅减少；尽管关键路径长度也有所降低，但改进程度相对较弱。
英文摘要
Hierarchical Task Network (HTN) planning is a powerful planning formalism based on task decomposition. Although most of the literature studied plan generation, comparatively less attention has been paid to post-plan optimization. In particular, plan deordering has been extensively studied in classical planning but remains under-researched in the HTN setting. Plan deordering removes unnecessary ordering constraints between actions in a plan whilst keeping the plan valid. In this paper, we adapt two established plan deordering techniques from classical planning by extending the techniques to account for hierarchical decomposition constraints. We evaluate our proposed approaches on the IPC 2023 Partial-Order HTN benchmarks and we compare them against Optiplan, an HTN planner that generates partially ordered plans directly. Our results show a substantial reduction in number of ordering constraints in both our implementations. Although we also observe a reduction in critical path length, the improvements are less pronounced.
