Neural heuristics for SAT solving
Not on the map. Why?
Abstract (arXiv)
We use neural graph networks with a message-passing architecture and an attention mechanism to enhance the branching heuristic in two SAT-solving algorithms. We report improvements of learned neural heuristics compared with two standard human-designed heuristics.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 May 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 2; 2023: 4.
7 papers.
- 2022Machine Learning Methods in Solving the Boolean Satisfiability Problemcited by 22
- 2021NeuroBack: Improving CDCL SAT Solving using Graph Neural Networkscited by 19
- 2022NSNet: A General Neural Probabilistic Framework for Satisfiability Problemscited by 18
- 2023G4SATBench: Benchmarking and Advancing SAT Solving with Graph Neural Networkscited by 17
- 2023Unveiling the Limits of Learned Local Search Heuristics: Are You the Mightiest of the Meek?cited by 1
- 2023Machine Learning for SAT: Restricted Heuristics and New Graph Representationscited by 0
- 2023Using deep learning to construct stochastic local search SAT solvers with performance boundscited by 0
- 2023Unveiling the Limits of Learned Local Search Heuristics: Are You the Mightiest of the Meek?30 Oct 2023
- 2023G4SATBench: Benchmarking and Advancing SAT Solving with Graph Neural Networks29 Sep 2023
- 2023Using deep learning to construct stochastic local search SAT solvers with performance bounds20 Sep 2023
- 2023Machine Learning for SAT: Restricted Heuristics and New Graph Representations18 Jul 2023
- 2022NSNet: A General Neural Probabilistic Framework for Satisfiability Problems7 Nov 2022
- 2022Machine Learning Methods in Solving the Boolean Satisfiability Problem2 Mar 2022
- 2021NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks26 Oct 2021