Conflict Driven Clause Learning

Conflict-Driven Clause Learning (CDCL) is a highly successful algorithm for solving the Boolean Satisfiability (SAT) problem, aiming to efficiently determine whether a logical formula has a satisfying assignment. Current research focuses on improving CDCL's performance through refined clause learning strategies, including adaptive restart and reset policies informed by reinforcement learning and the strategic management of learned clauses to avoid information overload. These advancements are impacting various fields, from automated theorem proving and neural network verification to circuit testing, by enabling faster and more efficient solutions to complex logical problems.