Co-Certificate Learning with SAT Modulo Symmetries
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.
Cite
Text
Kirchweger et al. "Co-Certificate Learning with SAT Modulo Symmetries." International Joint Conference on Artificial Intelligence, 2023. doi:10.24963/IJCAI.2023/216Markdown
[Kirchweger et al. "Co-Certificate Learning with SAT Modulo Symmetries." International Joint Conference on Artificial Intelligence, 2023.](https://mlanthology.org/ijcai/2023/kirchweger2023ijcai-co/) doi:10.24963/IJCAI.2023/216BibTeX
@inproceedings{kirchweger2023ijcai-co,
title = {{Co-Certificate Learning with SAT Modulo Symmetries}},
author = {Kirchweger, Markus and Peitl, Tomás and Szeider, Stefan},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2023},
pages = {1944-1953},
doi = {10.24963/IJCAI.2023/216},
url = {https://mlanthology.org/ijcai/2023/kirchweger2023ijcai-co/}
}