Satisfiability Problem Instance

Satisfiability problem instances (SAT instances) represent Boolean formulas, posing the challenge of determining whether a variable assignment exists that makes the formula true. Current research focuses on predicting the difficulty of solving these instances, employing techniques like analyzing strong backdoor sets and using machine learning models, including graph neural networks, to learn solver strategies and even generate new benchmark instances. These efforts aim to improve the efficiency and effectiveness of SAT solvers, impacting diverse fields that rely on Boolean satisfiability, such as automated reasoning, software verification, and artificial intelligence.