HTN Plan Repair Algorithms Compared: Strengths and Weaknesses of Different Methods
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908333145849856 |
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| author | Zaidins, Paul Goldman, Robert P. Kuter, Ugur Nau, Dana Roberts, Mark |
| author_facet | Zaidins, Paul Goldman, Robert P. Kuter, Ugur Nau, Dana Roberts, Mark |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16209 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HTN Plan Repair Algorithms Compared: Strengths and Weaknesses of Different Methods Zaidins, Paul Goldman, Robert P. Kuter, Ugur Nau, Dana Roberts, Mark Artificial Intelligence I.2.8 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. |
| title | HTN Plan Repair Algorithms Compared: Strengths and Weaknesses of Different Methods |
| topic | Artificial Intelligence I.2.8 |
| url | https://arxiv.org/abs/2504.16209 |