IJCAI 2023poster16 citations

Co-Certificate Learning with SAT Modulo Symmetries

Markus Kirchweger, Tomáš Peitl, Stefan Szeider

Abstract

We present a new SAT-based method for generating all graphs up to isomorphism that satisfy a given co-NP property. Our method extends the SAT Modulo Symmetry (SMS) framework with a technique that we call co-certificate learning. If SMS generates a candidate graph that violates the given co-NP property, we obtain a certificate for this violation, i.e., `co-certificate' for the co-NP property. The co-certificate gives rise to a clause that the SAT solver, serving as SMS's backend, learns as part of its CDCL procedure. We demonstrate that SMS plus co-certificate learning is a powerful method that allows us to improve the best-known lower bound on the size of Kochen-Specker vector systems, a problem that is central to the foundations of quantum mechanics and has been studied for over half a century. Our approach is orders of magnitude faster and scales significantly better than a recently proposed SAT-based method.

Constraint Satisfaction and Optimization: CSO: SatisfiabiltyConstraint Satisfaction and Optimization: CSO: ApplicationsConstraint Satisfaction and Optimization: CSO: Solvers and tools
BibTeX
@inproceedings{ijcai2023p216,
  title     = {Co-Certificate Learning with SAT Modulo Symmetries},
  author    = {Kirchweger, Markus and Peitl, Tomáš and Szeider, Stefan},
  booktitle = {Proceedings of the Thirty-Second International Joint Conference on
               Artificial Intelligence, {IJCAI-23}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Edith Elkind},
  pages     = {1944--1953},
  year      = {2023},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2023/216},
  url       = {https://doi.org/10.24963/ijcai.2023/216},
}
Co-Certificate Learning with SAT Modulo Symmetries · IJCAI 2023