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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.02035 |
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| _version_ | 1866912104329510912 |
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| author | Quenard, Gaspard Pellier, Damier Fiorino, Humbert |
| author_facet | Quenard, Gaspard Pellier, Damier Fiorino, Humbert |
| contents | This paper presents SibylSat, a novel SAT-based method designed to efficiently solve totally-ordered HTN problems (TOHTN). In contrast to prevailing SAT-based HTN planners that employ a breadth-first search strategy, SibylSat adopts a greedy search approach, enabling it to identify promising decompositions for expansion. The selection process is facilitated by a heuristic derived from solving a relaxed problem, which is also expressed as a SAT problem. Our experimental evaluations demonstrate that SibylSat outperforms existing SAT-based TOHTN approaches in terms of both runtime and plan quality on most of the IPC benchmarks, while also solving a larger number of problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02035 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SibylSat: Using SAT as an Oracle to Perform a Greedy Search on TOHTN Planning Quenard, Gaspard Pellier, Damier Fiorino, Humbert Artificial Intelligence This paper presents SibylSat, a novel SAT-based method designed to efficiently solve totally-ordered HTN problems (TOHTN). In contrast to prevailing SAT-based HTN planners that employ a breadth-first search strategy, SibylSat adopts a greedy search approach, enabling it to identify promising decompositions for expansion. The selection process is facilitated by a heuristic derived from solving a relaxed problem, which is also expressed as a SAT problem. Our experimental evaluations demonstrate that SibylSat outperforms existing SAT-based TOHTN approaches in terms of both runtime and plan quality on most of the IPC benchmarks, while also solving a larger number of problems. |
| title | SibylSat: Using SAT as an Oracle to Perform a Greedy Search on TOHTN Planning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2411.02035 |