Machine Learning Methods in Solving the Boolean Satisfiability Problem

• DOI: 10.1007/s11633-022-1396-2
• Publication Date: 2023-06-01T05:01:50Z
• Authors: Wenxuan Guo; Hui-Ling Zhen; Xijun Li; Wanqian Luo; Mingxuan Yuan; Yaohui Jin; Junchi Yan
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Logic in Computer Science; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Machine Learning (cs.LG); Logic in Computer Science (cs.LO)

This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT), an archetypal NP-complete problem, with the help of machine learning techniques. Despite the great success of modern SAT solvers to solve large industrial instances, the design of handcrafted heuristics is time-consuming and empirical. Under the circumstances, the flexible and expressive machine learning methods provide a proper alternative to solve this long-standing problem. We examine the evolving ML-SAT solvers from naive classifiers with handcrafted features to the emerging end-to-end SAT solvers such as NeuroSAT, as well as recent progress on combinations of existing CDCL and local search solvers with machine learning methods. Overall, solving SAT with machine learning is a promising yet challenging research topic. We conclude the limitations of current works and suggest possible future directions.