About this document
Machine Learning for SAT Solving by Habamenshi Yves is a document available to read on EtoBox.
This paper reviews the application of machine learning techniques to solve the Boolean satisfiability problem (SAT), highlighting the evolution from naive classifiers to advanced end-to-end SAT solvers like NeuroSAT. It discusses the integration of machine learning with existing SAT solving methods, including standalone solvers and enhancements to CDCL and local search solvers. The authors identify current limitations and suggest future research directions in this promising area of study.
- Author
- Habamenshi Yves
- Language
- EN