Conflict-driven clause learning SAT solvers

Frontiers in Artificial Intelligence and Applications

Volumn 185, Issue 1, 2009, Pages 131-153

  Marques Silva, Joao ^a   Lynce, Ines ^a   Malik, Sharad ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

DECISION THEORY; MODEL CHECKING;

BACKTRACK SEARCH; CLAUSE LEARNING; CONFLICTS ANALYSIS; INDUSTRIAL PROBLEM; LITERALS; PARADIGM SHIFTS; SAT SOLVERS; SEARCH PROCESS;

DATA STRUCTURES;

EID: 72949091469     PISSN: 09226389     EISSN: 18798314     Source Type: Book Series    
DOI: 10.3233/978-1-58603-929-5-131     Document Type: Article

References (65)

1. F. Aloul, A. Ramani, I. Markov, and K. A. Sakallah. Generic ILP versus specialized 0-1 ILP: An update. In International Conference on Computer-Aided Design, pages 450-457, November 2002.
2. F. Bacchus. Exploiting the computational tradeoff of more reasoning and less searching. In Fifth International Symposium on Theory and Applications of Satisfiability Testing, pages 7-16, May 2002.
3. P. Beame, H. A. Kautz, and A. Sabharwal. Towards understanding and harnessing the potential of clause learning. Journal of Artificial Intelligence Research, 22:319-351, 2004.
4. L. Baptista and J. P. Marques-Silva. Using randomization and learning to solve hard real-world instances of satisfiability. In International Conference on Principles and Practice of Constraint Programming, pages 489-494, September 2000.
5. R. I. Brafman. A simplifier for propositional formulas with many binary clauses. In International Joint Conference on Artificial Intelligence, August 2001.
6. M. Bruynooghe. Solving combinatorial search problems by intelligent backtracking. Information Processing Letters, 12(1):36-39, 1981.
7. R. Bayardo Jr. and R. Schrag. Using CSP look-back techniques to solve real-world SAT instances. In National Conference on Artificial Intelligence, pages 203-208, July 1997.
8. D. Chai and A. Kuehlmann. A fast pseudo-Boolean constraint solver. In Design Automation Conference, pages 830-835, June 2003.
9. R. Dechter. Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition. Artificial Intelligence, 41(3):273-312, January 1990.
10. R. Dechter. Enhancement schemes for constraint processing: Backjumping, learning, and cutset decomposition. Artificial Intelligence, 41(3):273-312, 1990.
11. N. Dershowitz, Z. Hanna, and A. Nadel. Towards a better understanding of the functionality of a conflict-driven SAT solver. In International Conference in Theory and Applications of Satisfiability Testing, pages 287-293, 2007.
12. M. Davis, G. Logemann, and D. Loveland. A machine program for theorem-proving. Communications of the ACM, 5:394-397, July 1962.
13. J. Doyle. A truth maintenance system. Artificial Intelligence, 12(3):231-272, 1979.
14. M. Davis and H. Putnam. A computing procedure for quantification theory. Journal of the ACM, 7:201-215, July 1960.
15. N. Éen and A. Biere. Effective preprocessing in SAT through variable and clause elimination. In International Conference in Theory and Applications of Satisfiability Testing, pages 61-75, 2005.
16. N. Éen and N. S̈orensson. Translating pseudo-Boolean constraints into SAT. Journal on Satisfiability, Boolean Modeling and Computa tion, 2:1-26, March 2006.
17. D. Frost and R. Dechter. Dead-end driven learning. In National Conference on Artificial Intelligence, pages 294-300, 1994.
18. J. Gaschnig. A general backtrack algorithm that eliminates most redundant tests. In International Joint Conference on Artificial Intelligence, page 457, 1977.
19. A. V. Gelder. Generalizations of watched literals for backtracking search. In International Symposium on Artificial Intelligence and Mathematics, January 2002.
20. A. V. Gelder. Verifying propositional unsatisfiability: Pitfalls to avoid. In International Conference on Theory and Applications of Satisfiability Testing, pages 328-333, 2007.
21. H. Ganzinger, G. Hagen, R. Nieuwenhuis, A. Oliveras, and C. Tinelli. DPLL(T): Fast decision procedures. In Computer-Aided Verification, pages 175-188, 2004.
22. M. L. Ginsberg. Dynamic backtracking. Journal of Artificial Intelligence Research, 1:25-46, 1993.
23. E. Goldberg and Y. Novikov. BerkMin: a fast and robust SATsolver. In Design, Automation and Testing in Europe Conference, pages 142-149, March 2002.
24. E. Giunchiglia, M. Narizzano, and A. Tacchella. Learning for quantified Boolean logic satisfiability. In National Conference on Artificial Intelligence, pages 649-654, August 2002.
25. C. P. Gomes, B. Selman, and N. Crato. Heavy-tailed distributions in combinatorial search. In International Conference on Principles and Practice of Constraint Programming, pages 121-135, 1997.
26. C. P. Gomes, B. Selman, and H. Kautz. Boosting combinatorial search through randomization. In National Conference on Artificial Intelligence, pages 431-437, July 1998.
27. J. F. Groote and J. P. Warners. The propositional formula checker heerhugo. In I. Gent, H. van Maaren, and T. Walsh, editors, SAT 2000, pages 261-281. IOS Press, 2000.
28. J. Huang. The effect of restarts on clause learning. In International Joint Conference on Artificial Intelligence, pages 2318-2323, 2007.
29. C. M. Li and Anbulagan. Look-ahead versus look-back for satisfiability problems. In International Conference on Principles and Practice of Constraint Programming, pages 341-355, October 1997.
30. R. Letz. Lemma and model caching in decision procedures for quantified Boolean formulas. In International Conference on Automated Reasoning with Analytic Tableau x and Related Methods, pages 160- 175, July 2002.
31. I. Lynce and J. P. Marques-Silva. Efficient data structures for backtrack search SAT solvers. Annals of Mathematics and Artificial Intelligence, 47(1):137-152, January 2005.
32. I. Lynce and J. P. Marques-Silva. Random backtracking in backtrack search algorithms for satisfiability. Discrete Applied Mathematics, 155(12):1604-1612, 2007.
33. M. D. T. Lewis, T. Schubert, and B. Becker. Speedup techniques utilized in modern SAT solvers. In International Conference in Theory and Applications of Satisfiability Testing, pages 437-443, 2005.
34. D. Le Berre, L. Simon, and O. Roussel. SAT competition. www.satcompetition.org.
35. M. Luby, A. Sinclair, and D. Zuckerman. Optimal speedup of Las Vegas algorithms. Information Processing Letters, 47(4):173-180, 1993.
36. K. L. McMillan. Interpolation and SAT-based model checking. In Computer-Aided Verification, 2003.
37. V. Manquinho and J. P. Marques-Silva. On using satisfiability-based pruning techniques in covering algorithms. In Design, Automation and Test in Europe Conference, pages 356-363, March 2000.
38. V. Manquinho and J. P. Marques-Silva. Search pruning conditions for Boolean optimization. In European Conference on Artificial Intelligence, pages 130-1107, August 2000.
39. V. M. Manquinho and J. P. Marques-Silva. Search pruning techniques in SAT-based branch-and-bound algorithms for the binate covering problem. IEEE Transactions on Computer-Aided Design, 21(5):505-516, May 2002.
40. V. Manquinho and J. P. Marques-Silva. Satisfiability-based algorithms for Boolean optimization. Annals of Mathematics and Artificial Intelligence, 40(3-4), March 2004.
41. M. Moskewicz, C. Madigan, Y. Zhao, L. Zhang, and S. Malik. Engineering an efficient SAT solver. In Design Automation Conference, pages 530-535, June 2001.
42. J. P. Marques-Silva. Search Algorithms for Satisfiability Problems in Combinational Switching Circuits. PhD thesis, University of Michigan, May 1995.
43. J. P. Marques-Silva. The impact of branching heuristics in propositional satisfiability algorithms. In Proceedings of the Portuguese Conference on Artificial Intelligence, pages 62-74, September 1999.
44. J. P. Marques-Silva. Algebraic simplification techniques for propositional satisfiability. In International Conference on Principles and Practice of Constraint Programming, pages 537-542, September 2000.
45. J. P. Marques-Silva and K. A. Sakallah. Dynamic search-space pruning techniques in path sensitization. In Design Automation Conference, pages 705-711, June 1994.
46. J. P. Marques-Silva and K. A. Sakallah. GRASP: A new search algorithm for satisfiability. In International Conference on Computer- Aided Design, pages 220-227, November 1996.
47. J. P. Marques-Silva and K. A. Sakallah. Robust search algorithms for test pattern generation. In Fault-Tolerant Computinig Symposium, pages 152-161, June 1997.
48. J. P. Marques-Silva and K. A. Sakallah. GRASP-A search algorithm for propositional satisfiability. IEEE Transactions on Computers, 48(5):506-521, May 1999.
49. J. P. Marques-Silva and K. A. Sakallah. Invited tutorial: Boolean satisfiability algorithms and applications in electronic design automation. In Computer Aided Verification, page 3, 2000.
50. A. Nadel. Backtrack search algorithms for propositional logic satisfiability: Review and innovations. Master's thesis, Hebrew University of Jerusalem, November 2002.
51. K. Pipatsrisawat and A. Darwiche. A lightweight component caching scheme for satisfiability solvers. In International Conference in Theory and Applications of Satisfiability Testing, pages 294-299, 2007.
52. S. Pilarski and G. Hu. SAT with partial clauses and back-leaps. In Design Automation Conference, pages 743-746, 2002.
53. L. M. Pereira and A. Porto. Selective bachtracking for logic programs. In Conference on Automated Deduction, pages 306-317, 1980.
54. P. Prosser. Hybrid algorithms for the constraint satisfaction problem. Computational Intelligence, 9:268-299, 1993.
55. L. Ryan. Efficient algorithms for clause-learning SAT solvers. Master's thesis, Simon Fraser University, February 2004.
56. R. M. Stallman and G. J. Sussman. Forward reasoning and dependency-directed backtracking in a system for computer-aided circuit analysis. Artificial Intelligence, 9(2):135-196, 1977.
57. O. Strichman. Pruning techniques for the SAT-based bounded model checking problem. In Advanced Research Working Conference on Correct Hardware Design and Verification Methods, pages 58-70, 2001.
58. R. E. Tarjan. Finding dominators in directed graphs. SIAM Journal on Computing, 1974.
59. H. Zhang. SATO: An efficient propositional prover. In Conference on Automated Deduction, pages 272-275, July 1997.
60. L. Zhang. Searching the Truth: Techniques for Satisfiability of Boolean Formulas. PhD thesis, Princeton University, June 2003.
61. R. Zabih and D. A. McAllester. A rearrangement search strategy for determining propositional satisfiability. In National Conference on Artificial Intelligence, pages 155-160, July 1988.
62. L. Zhang and S. Malik. Conflict driven learning in a quantified Boolean satisfiability solver. In International Conference on Computer-Aided Design, pages 442-449, 2002.
63. L. Zhang and S. Malik. Validating SAT solvers using an independent resolution-based checker: Practical implementations and other applications. In Design, Automation and Testing in Europe Conference, pages 10880-10885, 2003.
64. L. Zhang, C. F. Madigan, M. W. Moskewicz, and S. Malik. Efficient conflict driven learning in Boolean satisfiability solver. In International Conference on Computer-Aided Design, pages 279-285, 2001.
65. H. Zhang and M. Stickel. Implementing the Davis-Putnam method. In I. Gent, H. van Maaren, and T. Walsh, editors, SAT 2000, pages 309-326. IOS Press, 2000.