Employing Machine Learning Models to Solve Uniform Random 3-SAT

Abstract

We have employed a chosen set of machine learning models to solve the 3-CNF-SAT problem. Through f1-scores, we obtain how these algorithms perform at solving the problem as a classification task. The implication of this endeavour is exciting given the property of the NP-complete class problems being polynomial-time reducible to each other.