Preliminary Results on Exploration-Driven Satisfiability Solving

Md. Solimul Chowdhury, Martin Müller, Jia-Huai You

AAAI 2018 pp. 8069-8070

doi:10.1609/AAAI.V32I1.12164 /aaai/2018/chowdhury2018aaai-preliminary/

Abstract

In this abstract, we present our study of exploring the SAT search space via random-sampling, with the goal of improving Conflict Directed Clause Learning (CDCL) SAT solvers. Our proposed CDCL SAT solving algorithm expSAT uses a novel branching heuristic expVSIDS. It combines the standard VSIDS scores with heuristic scores derived from exploration. Experiments with application benchmarks from recent SAT competitions demonstrate the potential of the expSAT approach for improving CDCL SAT solvers.

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Cite

Text

Chowdhury et al. "Preliminary Results on Exploration-Driven Satisfiability Solving." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.12164

Markdown

[Chowdhury et al. "Preliminary Results on Exploration-Driven Satisfiability Solving." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/chowdhury2018aaai-preliminary/) doi:10.1609/AAAI.V32I1.12164

BibTeX

@inproceedings{chowdhury2018aaai-preliminary,
  title     = {{Preliminary Results on Exploration-Driven Satisfiability Solving}},
  author    = {Chowdhury, Md. Solimul and Müller, Martin and You, Jia-Huai},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {8069-8070},
  doi       = {10.1609/AAAI.V32I1.12164},
  url       = {https://mlanthology.org/aaai/2018/chowdhury2018aaai-preliminary/}
}