Academic Journal
Learning Efficiency Meets Symmetry Breaking
| Title: | Learning Efficiency Meets Symmetry Breaking |
|---|---|
| Authors: | Bai, Yingbin, Thiébaux, Sylvie, Trevizan, Felipe |
| Contributors: | Thiébaux, Sylvie |
| Source: | Proceedings of the International Conference on Automated Planning and Scheduling. 35:154-159 |
| Publication Status: | Preprint |
| Publisher Information: | Association for the Advancement of Artificial Intelligence (AAAI), 2025. |
| Publication Year: | 2025 |
| Subject Terms: | [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Machine Learning (cs.LG) |
| Description: | 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. |
| Document Type: | Article Conference object |
| File Description: | application/pdf |
| ISSN: | 2334-0843 2334-0835 |
| DOI: | 10.1609/icaps.v35i1.36112 |
| DOI: | 10.48550/arxiv.2504.19738 |
| Access URL: | http://arxiv.org/abs/2504.19738 https://hal.science/hal-05226738v1 |
| Rights: | CC BY |
| Accession Number: | edsair.doi.dedup.....0c37dfa07b0481f37267837103e096b3 |
| Database: | OpenAIRE |
| ISSN: | 23340843 23340835 |
|---|---|
| DOI: | 10.1609/icaps.v35i1.36112 |