ML Anthology

A Verified SAT Solver Framework with Learn, Forget, Restart, and Incrementality

Jasmin Christian Blanchette, Mathias Fleury, Christoph Weidenbach
IJCAI 2017 pp. 4786-4790
doi:10.24963/IJCAI.2017/667 /ijcai/2017/blanchette2017ijcai-verified/

Abstract

We developed a formal framework for SAT solving using the Isabelle/HOL proof assistant. Through a chain of refinements, an abstract CDCL (conflict-driven clause learning) calculus is connected to a SAT solver that always terminates with correct answers. The framework offers a convenient way to prove theorems about the SAT solver and experiment with variants of the calculus. Compared with earlier verifications, the main novelties are the inclusion of the CDCL rules for forget, restart, and incremental solving and the use of refinement.

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Cite

Text

Blanchette et al. "A Verified SAT Solver Framework with Learn, Forget, Restart, and Incrementality." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/667

Markdown

[Blanchette et al. "A Verified SAT Solver Framework with Learn, Forget, Restart, and Incrementality." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/blanchette2017ijcai-verified/) doi:10.24963/IJCAI.2017/667

BibTeX

@inproceedings{blanchette2017ijcai-verified,
  title     = {{A Verified SAT Solver Framework with Learn, Forget, Restart, and Incrementality}},
  author    = {Blanchette, Jasmin Christian and Fleury, Mathias and Weidenbach, Christoph},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {4786-4790},
  doi       = {10.24963/IJCAI.2017/667},
  url       = {https://mlanthology.org/ijcai/2017/blanchette2017ijcai-verified/}
}