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Learning Efficiency Meets Symmetry Breaking

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This paper demonstrates improved planning efficiency by integrating pruning methods and symmetry detection with graph neural networks.

Improving learning efficiency with the introduction of symmetry detection techniques enhances planning outcomes.
Utilizing action pruning and state pruning methods allows effective management of symmetries in search spaces.
Integrating these techniques into Fast Downward shows success over LAMA in the IPC learning track dataset.
The exploration of symmetry breaking in planning problems reveals new avenues for graph neural networks.

Bai et al. (2025) studied this question.

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View Full Paper

Ask AI

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Authors

This paper demonstrates improved planning efficiency by integrating pruning methods and symmetry detection with graph neural networks.

Improving learning efficiency with the introduction of symmetry detection techniques enhances planning outcomes.
Utilizing action pruning and state pruning methods allows effective management of symmetries in search spaces.
Integrating these techniques into Fast Downward shows success over LAMA in the IPC learning track dataset.
The exploration of symmetry breaking in planning problems reveals new avenues for graph neural networks.

Bai et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark