Learning Efficiency Meets Symmetry Breaking
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915263034687488 |
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| author | Bai, Yingbin Thiebaux, Sylvie Trevizan, Felipe |
| author_facet | Bai, Yingbin Thiebaux, Sylvie Trevizan, Felipe |
| contents | Learning-based planners leveraging Graph Neural Networks can learn search guidance applicable to large search spaces, yet their potential to address symmetries remains largely unexplored. In this paper, we introduce a graph representation of planning problems allying learning efficiency with the ability to detect symmetries, along with two pruning methods, action pruning and state pruning, designed to manage symmetries during search. The integration of these techniques into Fast Downward achieves a first-time success over LAMA on the latest IPC learning track dataset. Code is released at: https://github.com/bybeye/Distincter. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_19738 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Efficiency Meets Symmetry Breaking Bai, Yingbin Thiebaux, Sylvie Trevizan, Felipe Artificial Intelligence Machine Learning Learning-based planners leveraging Graph Neural Networks can learn search guidance applicable to large search spaces, yet their potential to address symmetries remains largely unexplored. In this paper, we introduce a graph representation of planning problems allying learning efficiency with the ability to detect symmetries, along with two pruning methods, action pruning and state pruning, designed to manage symmetries during search. The integration of these techniques into Fast Downward achieves a first-time success over LAMA on the latest IPC learning track dataset. Code is released at: https://github.com/bybeye/Distincter. |
| title | Learning Efficiency Meets Symmetry Breaking |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2504.19738 |