Symmetry Detection and Breaking in Linear Cost-Optimal Numeric Planning


  • Alexander Shleyfman Bar-Ilan University
  • Ryo Kuroiwa University of Toronto
  • J. Christopher Beck University of Toronto



Planning and scheduling with mixed continuous and discrete states/actions/decisions, Heuristic search


One of the main challenges of domain-independent numeric planning is the complexity of the search problem. The exploitation of structural symmetries in a search problem can constitute an effective method of pruning search branches that may lead to exponential improvements in performance. For over a decade, symmetry breaking techniques have been successfully used within both optimal and satisficing classical planning. In this work, we show that symmetry detection methods applied in classical planning with some effort can be modified to detect symmetries in linear numeric planning. The detected symmetry group, thereafter, can be used almost directly in the A*-based symmetry breaking algorithms such as DKS and Orbit Space Search. We empirically validate that symmetry pruning can yield a substantial reduction in the search effort, even if algorithms are equipped with a strong heuristic, such as LM-cut.




How to Cite

Shleyfman, A., Kuroiwa, R., & Beck, J. C. (2023). Symmetry Detection and Breaking in Linear Cost-Optimal Numeric Planning. Proceedings of the International Conference on Automated Planning and Scheduling, 33(1), 393-401.