Machine Learning Methods in Solving the Boolean Satisfiability Problem

This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT), an archetypal N P -complete problem, with the aid of machine learning (ML) techniques. Over the last decade, the machine learning society advances rapidly and surpasses human performance on several tasks. T...

Full description

Saved in:
Bibliographic Details
Published in:Machine Intelligence Research Vol. 20; no. 5; pp. 640 - 655
Main Authors: Guo, Wenxuan, Zhen, Hui-Ling, Li, Xijun, Luo, Wanqian, Yuan, Mingxuan, Jin, Yaohui, Yan, Junchi
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2023
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Automation
Boolean
Computer Science
Deep learning
Human performance
Integer programming
Machine learning
Neural networks
Review
Solvers
deep learning
combinatorial optimization
Machine learning (ML)
graph neural networks (GNNs)
Boolean satisfiability (SAT)
ISSN:2731-538X, 2153-182X, 2731-5398, 2153-1838
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT), an archetypal N P -complete problem, with the aid of machine learning (ML) techniques. Over the last decade, the machine learning society advances rapidly and surpasses human performance on several tasks. This trend also inspires a number of works that apply machine learning methods for SAT solving. In this survey, we examine the evolving ML SAT solvers from naive classifiers with handcrafted features to emerging end-to-end SAT solvers, as well as recent progress on combinations of existing conflict-driven clause learning (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. The collected paper list is available at https://github.com/Thinklab-SJTU/awesome-ml4co .
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2731-538X
2153-182X
2731-5398
2153-1838
DOI:10.1007/s11633-022-1396-2