Double Configuration Checking in Stochastic Local Search for Satisfiability


  • Chuan Luo Peking University
  • Shaowei Cai Chinese Academy of Sciences
  • Wei Wu Peking University
  • Kaile Su Peking University



Double Configuration Checking, Stochastic Local Search, Satisfiability


Stochastic local search (SLS) algorithms have shown effectiveness on satisfiable instances of the Boolean satisfiability (SAT) problem. However, their performance is still unsatisfactory on random k-SAT at the phase transition, which is of significance and is one of the empirically hardest distributions of SAT instances. In this paper, we propose a new heuristic called DCCA, which combines two configuration checking (CC) strategies with different definitions of configuration in a novel way. We use the DCCA heuristic to design an efficient SLS solver for SAT dubbed DCCASat. The experiments show that the DCCASat solver significantly outperforms a number of state-of-the-art solvers on extensive random k-SAT benchmarks at the phase transition. Moreover, DCCASat shows good performance on structured benchmarks, and a combination of DCCASat with a complete solver achieves state-of-the-art performance on structured benchmarks.




How to Cite

Luo, C., Cai, S., Wu, W., & Su, K. (2014). Double Configuration Checking in Stochastic Local Search for Satisfiability. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).



Main Track: Search and Constraint Satisfaction