In the contemporary data-driven era, the fields of machine learning, deep learning, big data,
statistics, and data science are essential for forecasting outcomes and getting insights from …

Automated Program Repair (APR) helps improve the efficiency of software development and
maintenance. Recent APR techniques use deep learning, particularly the encoder-decoder …

Constraint solving is applied in different application contexts. Examples thereof are the
configuration of complex products and services, the determination of production schedules …

This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT),
an archetypal NP-complete problem, with the aid of machine learning (ML) techniques. Over …

Industrial SAT formula generation is a critical yet challenging task. Existing SAT generation
approaches can hardly simultaneously capture the global structural properties and maintain …

We propose a universal Graph Neural Network architecture which can be trained as an end-
2-end search heuristic for any Constraint Satisfaction Problem (CSP). Our architecture can …

Abstract We present the Neural Satisfiability Network (NSNet), a general neural framework
that models satisfiability problems as probabilistic inference and meanwhile exhibits proper …

Partial maximum satisfiability (PMS) is a significant generalization of Boolean satisfiability
(SAT) and maximum satisfiability (MaxSAT), by introducing hard clauses and soft clauses …

Propositional satisfiability problem (SAT) is represented in a conjunctive normal form with
multiple clauses, which is an important non-deterministic polynomial-time (NP) complete …

We present DeepSAT, a novel end-to-end learning framework for the Boolean satisfiability
(SAT) problem. Unlike existing solutions trained on random SAT instances with relatively …