Incorporating Multi-armed Bandit with Local Search for MaxSAT
This work addresses optimization in constraint satisfaction for AI and computer science, offering incremental improvements to existing local search methods.
The paper tackles the Partial MaxSAT and Weighted MaxSAT problems by proposing a local search algorithm, BandHS, that uses multi-armed bandits to guide search directions and an initialization method prioritizing unit and binary clauses. The result shows excellent performance, greatly boosting state-of-the-art algorithms like SATLike3.0 and NuWLS-c.
Partial MaxSAT (PMS) and Weighted PMS (WPMS) are two practical generalizations of the MaxSAT problem. In this paper, we propose a local search algorithm for these problems, called BandHS, which applies two multi-armed bandits to guide the search directions when escaping local optima. One bandit is combined with all the soft clauses to help the algorithm select to satisfy appropriate soft clauses, and the other bandit with all the literals in hard clauses to help the algorithm select appropriate literals to satisfy the hard clauses. These two bandits can improve the algorithm's search ability in both feasible and infeasible solution spaces. We further propose an initialization method for (W)PMS that prioritizes both unit and binary clauses when producing the initial solutions. Extensive experiments demonstrate the excellent performance and generalization capability of our proposed methods, that greatly boost the state-of-the-art local search algorithm, SATLike3.0, and the state-of-the-art SAT-based incomplete solver, NuWLS-c.