对比三种层次化计划修复算法，揭示其适用场景差异。
HTN Plan Repair Algorithms Compared: Strengths and Weaknesses of Different Methods
- 基于不同修复问题定义，构建三类算法框架。
- 实测显示算法在修复覆盖和运行时表现有显著差异。
- 适合需高效修复或复杂回溯的规划系统开发者参考。
本文对三种近期的层次化计划修复算法——SHOPFixer、IPyHOPPER 和 Rewrite 进行了理论与实证比较。理论分析表明，三者对应三种不同的计划修复问题定义，导致搜索空间、可解决的修复问题类型及修复方式存在差异。理解这些区别对选择合适修复方法至关重要。基于理论结果，我们在一系列基准规划问题上评估了算法性能。实证结果进一步揭示了各系统在运行时修复效率与修复问题覆盖范围上的差异，这些差异与算法特性如重规划、时间顺序回溯及计划树上的回跳机制密切相关。
原文摘要 · Abstract (English)
This paper provides theoretical and empirical comparisons of three recent hierarchical plan repair algorithms: SHOPFixer, IPyHOPPER, and Rewrite. Our theoretical results show that the three algorithms correspond to three different definitions of the plan repair problem, leading to differences in the algorithms' search spaces, the repair problems they can solve, and the kinds of repairs they can make. Understanding these distinctions is important when choosing a repair method for any given application. Building on the theoretical results, we evaluate the algorithms empirically in a series of benchmark planning problems. Our empirical results provide more detailed insight into the runtime repair performance of these systems and the coverage of the repair problems solved, based on algorithmic properties such as replanning, chronological backtracking, and backjumping over plan trees.
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