CNNSAT: Fast, Accurate Boolean Satisfiability using Convolutional Neural Networks

Sep 27, 2018 ICLR 2019 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: Boolean satisfiability (SAT) is one of the most well-known NP-complete problems and has been extensively studied. State-of-the-art solvers exist and have found a wide range of applications. However, they still do not scale well to formulas with hundreds of variables. To tackle this fundamental scalability challenge, we introduce CNNSAT, a fast and accurate statistical decision procedure for SAT based on convolutional neural networks. CNNSAT's effectiveness is due to a precise and compact representation of Boolean formulas. On both real and synthetic formulas, CNNSAT is highly accurate and orders of magnitude faster than the state-of-the-art solver Z3. We also describe how to extend CNNSAT to predict satisfying assignments when it predicts a formula to be satisfiable.
  • Keywords: Convolutional Neural Networks, Boolean satisfiability problem, Satisfiability modulo theories
  • TL;DR: We introduce CNNSAT, a fast and accurate statistical decision procedure for SAT based on convolutional neural networks.
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