GRASP is a new search algorithm for Propositional Satisfiability (SAT) that incorporates powerful search-pruning techniques. It is based on the inevitability of conflicts during search and uses conflict analysis to backtrack nonchronologically, potentially pruning large portions of the search space. By recording the causes of conflicts, GRASP can preempt similar conflicts and identify necessary assignments for a solution. Experimental results show that GRASP is highly effective for many SAT instances. The paper introduces GRASP, a configurable framework for solving SAT problems. It is based on the idea that conflicts during search provide opportunities to improve the problem description by adding conflict-induced clauses. These clauses help prune the search space in three ways: enabling nonchronological backtracking, preempting conflicts, and identifying necessary assignments. GRASP uses backtracking search with Boolean Constraint Propagation (BCP) to explore the search space. It includes a decision engine, a deduction engine, and a diagnosis engine. The decision engine selects variable assignments, the deduction engine applies BCP to derive implications, and the diagnosis engine analyzes conflicts to generate new clauses and backtracking levels. Conflict analysis in GRASP involves identifying the causes of conflicts and generating new clauses to prevent future conflicts. This includes failure-driven assertions, nonchronological backtracking, and conflict-based equivalence. The diagnosis engine creates conflict-induced clauses that help prune the search space and improve efficiency. Experimental results show that GRASP outperforms several state-of-the-art SAT algorithms on most benchmarks. It is particularly effective for practical classes of SAT instances, where local search algorithms may be inadequate. GRASP's ability to handle conflicts and generate new clauses makes it more efficient than other algorithms. The paper also discusses variations of the diagnosis engine, including space-bounded diagnosis engines and unique implication points (UIPs). These improvements help reduce the size of the clause database and generate stronger implicates, enhancing the efficiency of the search process. Overall, GRASP is a powerful SAT algorithm that effectively handles conflicts and improves search efficiency through conflict analysis and nonchronological backtracking. Experimental results demonstrate its effectiveness for a wide range of SAT instances.GRASP is a new search algorithm for Propositional Satisfiability (SAT) that incorporates powerful search-pruning techniques. It is based on the inevitability of conflicts during search and uses conflict analysis to backtrack nonchronologically, potentially pruning large portions of the search space. By recording the causes of conflicts, GRASP can preempt similar conflicts and identify necessary assignments for a solution. Experimental results show that GRASP is highly effective for many SAT instances. The paper introduces GRASP, a configurable framework for solving SAT problems. It is based on the idea that conflicts during search provide opportunities to improve the problem description by adding conflict-induced clauses. These clauses help prune the search space in three ways: enabling nonchronological backtracking, preempting conflicts, and identifying necessary assignments. GRASP uses backtracking search with Boolean Constraint Propagation (BCP) to explore the search space. It includes a decision engine, a deduction engine, and a diagnosis engine. The decision engine selects variable assignments, the deduction engine applies BCP to derive implications, and the diagnosis engine analyzes conflicts to generate new clauses and backtracking levels. Conflict analysis in GRASP involves identifying the causes of conflicts and generating new clauses to prevent future conflicts. This includes failure-driven assertions, nonchronological backtracking, and conflict-based equivalence. The diagnosis engine creates conflict-induced clauses that help prune the search space and improve efficiency. Experimental results show that GRASP outperforms several state-of-the-art SAT algorithms on most benchmarks. It is particularly effective for practical classes of SAT instances, where local search algorithms may be inadequate. GRASP's ability to handle conflicts and generate new clauses makes it more efficient than other algorithms. The paper also discusses variations of the diagnosis engine, including space-bounded diagnosis engines and unique implication points (UIPs). These improvements help reduce the size of the clause database and generate stronger implicates, enhancing the efficiency of the search process. Overall, GRASP is a powerful SAT algorithm that effectively handles conflicts and improves search efficiency through conflict analysis and nonchronological backtracking. Experimental results demonstrate its effectiveness for a wide range of SAT instances.