On the power of clause-learning SAT solvers as resolution engines

Artificial Intelligence

Volumn 175, Issue 2, 2011, Pages 512-525

  Pipatsrisawat, Knot ^a   Darwiche, Adnan ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Boolean satisfiability; Clause learning SAT solvers; DPLL; Proof complexity; Resolution proof

Indexed keywords

BOOLEAN SATISFIABILITY; DPLL; PROOF COMPLEXITY; RESOLUTION PROOFS; SAT SOLVERS;

FORMAL LOGIC;

EID: 78650964571     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artint.2010.10.002     Document Type: Article

References (30)

1. A. Atserias, J.K. Fichte, M. Thurley, Clause-learning algorithms with many restarts and bounded-width resolution, in: Proceedings of 12th International Conference on Theory and Applications of Satisfiability Testing (SAT'09), 2009, pp. 114-127.
2. G. Audemard, L. Bordeaux, Y. Hamadi, S. Jabbour, L. Sais, A generalized framework for conflict analysis, in: Proceedings of 11th International Conference on Theory and Applications of Satisfiability Testing (SAT'08), 2008, pp. 21-27.
3. G. Audemard, L. Simon, Predicting learnt clauses quality in modern SAT solver, in: Proceedings of 21st International Joint Conference on Artificial Intelligence (IJCAI'09), July 2009, pp. 399-404.
4. R.J.J. Bayardo, R.C. Schrag, Using CSP look-back techniques to solve real-world SAT instances, in: Proceedings of 14th National Conference on Artificial Intelligence (AAAI-97), 1997, pp. 203-208.
5. P. Beame, H. Kautz, and A. Sabharwal Towards understanding and harnessing the potential of clause learning Journal of Artificial Intelligence Research 22 2004 319 351
6. E. Ben-Sasson, and A. Wigderson Short proofs are narrow-resolution made simple Journal of the ACM 48 2 2001 149 169
7. S.R. Buss, J. Hoffmann, and J. Johannsen Resolution trees with lemmas: Resolution refinements that characterize DLL algorithms with clause learning Logical Methods in Computer Science 4 2008 4
8. S.A. Cook, and R.A. Reckhow The relative efficiency of propositional proof systems Journal of Symbolic Logic 44 1 1979 36 50
9. M. Davis, G. Logemann, and D. Loveland A machine program for theorem-proving Commun. ACM 5 7 1962 394 397
10. N. Eén, and N. Sörensson MiniSat v2.0 (Beta) solver description http://fmv.jku.at/sat-race-2006/descriptions/27-minisat2.pdf
11. N. Eén, N. Sörensson, An extensible SAT-solver, in: SAT'03, 2003, pp. 502-518.
12. E. Goldberg, Y. Novikov, Berkmin: A fast and robust SAT solver, in: Proceedings of the Design Automation and Test in Europe (DATE'02), 2002, pp. 142-149.
13. E. Goldberg, Y. Novikov, Verification of proofs of unsatisfiability for CNF formulas, in: Proceedings of the Design Automation and Test in Europe (DATE'03), 2003, pp. 10886-10891.
14. C.P. Gomes, B. Selman, N. Crato, Heavy-tailed distributions in combinatorial search, in: Proceedings of 3rd International Conference on Principles and Practice of Constraint Programming (CP-97), 1997, pp. 121-135.
15. P. Hertel, F. Bacchus, T. Pitassi, A. Van Gelder, Clause learning can effectively p-simulate general propositional resolution, in: Proceedings of 23rd AAAI Conference on Artificial Intelligence (AAAI-08), 2008, pp. 283-290.
16. J. Huang, A case for simple SAT solvers, in: Proceedings of the 13th International Conference on Principles and Practice of Constraint Programming (CP-07), 2007, pp. 839-846.
17. J. Huang, The effect of restarts on the efficiency of clause learning, in: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI-07), 2007, pp. 2318-2323.
18. M. Luby, A. Sinclair, and D. Zuckerman Optimal speedup of Las Vegas algorithms Information Processing Letters 47 1993 173 180
19. Y. Mahajan, Z. Fu, S. Malik, zchaff2004: An efficient SAT solver, in: Proceedings of 7th International Conference on Theory and Applications of Satisfiability Testing (SAT'04) (Selected Papers), 2004, pp. 360-375.
20. J.P. Marques-Silva, K.A. Sakallah, GRASP - A new search algorithm for satisfiability, in: Proceedings of International Conference on Computer-Aided Design (ICCAD '96), 1996, pp. 220-227.
21. M. Moskewicz, C. Madigan, Y. Zhao, L. Zhang, S. Malik, Chaff: Engineering an efficient SAT solver, in: Proceedings of 38th Design Automation Conference (DAC'01), 2001.
22. A. Nadel, M. Gordon, A. Palti, Z. Hanna, Eureka-2006 SAT solver, Solver description for SAT-Race, 2006.
23. K. Pipatsrisawat, A. Darwiche, RSat 2.0: SAT solver description, 2007.
24. K. Pipatsrisawat, A. Darwiche, A new clause learning scheme for efficient unsatisfiability proofs, in: Proceedings of 23rd AAAI Conference on Artificial Intelligence (AAAI-08), 2008, pp. 1481-1484.
25. K. Pipatsrisawat, A. Darwiche, On the power of clause-learning SAT solvers with restarts, in: Proceedings of the 15th International Conference on Principles and Practice of Constraint Programming (CP-09), September 2009, pp. 654-668.
26. J.A. Robinson A machine-oriented logic based on the resolution principle Journal of the ACM 12 1 1965 23 41
27. L. Ryan, Efficient algorithms for clause-learning SAT solvers, Master's thesis, Simon Fraser University, 2004.
28. T. Sang, P. Beame, H. Kautz, Heuristics for fast exact model counting, in: Proceedings of the 8th International Conference on Theory and Applications of Satisfiability Testing (SAT'05), 2005, pp. 226-240.
29. A. Van Gelder, Pool resolution and its relation to regular resolution and DPLL with clause learning, in: Proceedings of 12th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning (LPAR'05), 2005, pp. 580-594.
30. L. Zhang, C.F. Madigan, M.W. Moskewicz, S. Malik, Efficient conflict driven learning in boolean satisfiability solver, in: Proceedings of International Conference on Computer-Aided Design (ICCAD'01), 2001, pp. 279-285.