Decision Level Reward Based Branching Heuristic in Maple Solver

Abstract

The SAT problem is one of basic issues of artificial intelligence and computer science. Maple solver is an algorithm solver that specializes in solving SAT problems. In order to improve the efficiency of the solver, decision level reward based branching heuristic was proposed. Firstly, this paper introduces its major framework and two excellent branching heuristics: Variable State Independent Decaying Sum(VSIDS) Decision Heuristic and Learning Rate Based(LRB) Branching Heuristic. Then, a new method named DLR is proposed in view of LRB considering the decision level rate. Finally, experimental results of different sets of instances indicate that the Maple solver with DLR strategy outperforms original version with LRB strategy by reducing the number of conflicts and decisions.