Towards understanding and harnessing the potential of clause learning

Journal of Artificial Intelligence Research

Volumn 22, Issue , 2004, Pages 319-351

  Beame, Paul ^a   Kautz, Henry ^a   Sabharwal, Ashish ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BOOLEAN ALGEBRA; LEARNING ALGORITHMS; PROBLEM SOLVING; RANDOM PROCESSES;

BOOLEAN SATISFIABILITY; CLAUSE LEARNING; DAVIS-PUTNAM RESOLUTION; RANDOMIZED PEBBLING PROBLEMS;

LEARNING SYSTEMS;

EID: 27344452517     PISSN: 10769757     EISSN: 10769757     Source Type: Journal    
DOI: 10.1613/jair.1410     Document Type: Article

References (50)

1. Achlioptas, D., Beame, P., & Molloy, M. (2001). A sharp threshold in proof complexity. In Proceedings of the Thirty-Third Annual ACM Symposium on Theory of Computing, pp. 337-346, Crete, Greece.
2. Alekhnovich, M., Johannsen, J., Pitassi, T., & Urquhart, A. (2002). An exponential separation between regular and general resolution. In Proceedings of the Thirty-Fourth Annual ACM Symposium on Theory of Computing, pp. 448-456, Montréal, Canada.
3. Aloul, F. A., Ramani, A., Markov, I. L., & Sakallah, K. A. (2002). Solving difficult SAT instances in the presence of symmetry. In Proceedings of the 39th Design Automation Conference, pp. 731-736, New Orleans, LA. ACM.
4. Amir, E., & McIlraith, S. A. (2000). Partition-based logical reasoning. In Proceedings of the 7th International Conference on Principles of Knowledge Representation and Reasoning, pp. 389-400, Breckenridge, CO.
5. Atserias, A., & Bonet, M. L. (2002). On the automatizability of resolution and related propositional proof systems. In CSL '02: 16th Workshop on Computer Science Logic, Vol. 2471 of Lecture Notes in Computer Science, pp. 569-583, Edinburgh, Scotland. Springer.
6. Aziz, A., Tasiran, S., & Brayton, R. K. (1994). BDD variable orderings for interacting finite state machines. In Proceedings of the 31th Design Automation Conference, pp. 283-288, San Diego, CA. ACM.
7. Baptista, L., & Silva, J. P. M. (2000). Using randomization and learning to solve hard real-world instances of satisfiability. In 6th Principles and Practice of Constraint Programming, pp. 489-494.
8. Bayardo Jr., R. J., & Schrag, R. C. (1997). Using CST look-back techniques to solve real-world SAT instances. In Proceedings, AAAI-97: 14th National Conference on Artificial Intelligence, pp. 203-208, Providence, Rhode Island.
9. Beame, P., Impagliazzo, R., Pitassi, T., & Segerlind, N. (2003a). Memoization and DPLL: Formula caching proof systems. In Proceedings 18th Annual IEEE Conference on Computational Complexity, pp. 225-236, Aarhus, Denmark.
10. Beame, P., Kautz, H., & Sabharwal, A. (2003b). Understanding the power of clause learning. In Proceedings of the 18th International Joint Conference on Artificial Intelligence, pp. 1194-1201, Acapulco, Mexico.
11. Ben-Sasson, E., Impagliazzo, R., & Wigderson, A. (2000). Near-optimal separation of tree-like and general resolution. Tech. rep. TR00-005, Electronic Colloquium in Computation Complexity, http://www.eccc.uni-trier.de/eccc/.
12. To appear in Combinatorica.
13. Biere, A., Cimatti, A., Clarke, E. M., Fujita, M., & Zhu, Y. (1999a). Symbolic model checking using SAT procedures instead of BDDs. In Proceedings of the 36th Design Automation Conference, pp. 317-320, New Orleans, LA. ACM.
14. Biere, A., Cimatti, A., Clarke, E. M., & Zhu, Y. (1999b). Symbolic model checking without BDDs. In 5th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, pp. 193-207, Amsterdam, the Netherlands.
15. Bonet, M. L., Esteban, J. L., Galesi, N., & Johansen, J. (2000). On the relative complexity of resolution refinements and cutting planes proof systems. SIAM Journal on Computing, 30(5), 1462-1484.
16. Bonet, M. L., & Galesi, N. (2001). Optimality of size-width tradeoffs for resolution. Computational Complexity, 10(4), 261-276.
17. Brafman, R. I. (2001). A simplifier for propositional formulas with many binary clauses. In Proceedings of the 17th International Joint Conference on Artificial Intelligence, pp. 515-522, Seattle, WA.
18. Buresh-Oppenheim, J., & Pitassi, T. (2003). The complexity of resolution refinements. In 18th Annual IEEE Symposium on Logic in Computer Science, pp. 138-147, Ottawa, Canada.
19. Cook, S. A., & Reckhow, R. A. (1977). The relative efficiency of propositional proof systems. Journal of Symbolic Logic, 44(1), 36-50.
20. Davis, M., Logemann, G., & Loveland, D. (1962). A machine program for theorem proving. Communications of the ACM, 5, 394-397.
21. Davis, M., & Putnam, H. (1960). A computing procedure for quantification theory. Communications of the ACM, 7, 201-215.
22. Davis, R. (1984). Diagnostic reasoning based on structure and behavior. Artificial Intelligence, 24, 347-410.
23. de Kleer, J., & Williams, B. C. (1987). Diagnosing multiple faults. Artificial Intelligence, 32(1), 97-130.
24. Esteban, J. L., Galesi, N., & Messner, J. (2002). On the complexity of resolution with bounded conjunctions. In Automata, Languages, and Programming: 29th International Colloquium, Vol. 2380 of Lecture Notes in Computer Science, pp. 220-231, Malaga, Spain. Springer-Verlag.
25. Genesereth, R. (1984). The use of design descriptions in automated diagnosis. Artificial Intelligence, 24, 411-436.
26. Giunchiglia, E., Maratea, M., & Tacchella, A. (2002). Dependent and independent variables in propositional satisfiability. In Proceedings of the 8th European Conference on Logics in Artificial Intelligence (JELIA), Vol. 2424 of Lecture Notes in Computer Science, pp. 296-307, Cosenza, Italy. Springer-Verlag.
27. Gomes, C. P., Selman, B., & Kautz, H. (1998a). Boosting combinatorial search through randomization. In Proceedings, AAAI-98: 15th National Conference on Artificial Intelligence, pp. 431-437, Madison, WI.
28. Gomes, C. P., Selman, B., McAloon, K., & Tretkoff, C. (1998b). Randomization in back-track search: Exploiting heavy-tailed profiles for solving hard scheduling problems. In Proceedings of the 4th International Conference on Artificial Intelligence Planning Systems, Pittsburgh, PA.
29. Haken, A. (1985). The intractability of resolution. Theoretical Computer Science, 39, 297-305.
30. Harlow, J. E., & Brglez, F. (1998). Design of experiments in BDD variable ordering: Lessons learned. In Proceedings of the International Conference on Computer Aided Design, pp. 646-652, San Jose, CA. IEEE/ACM.
31. Johnson, D. S., & Trick, M. A. (Eds.). (1996). Cliques, Coloring and Satisfiability: Second DIMACS Implementation Challenge, Vol. 26 of DIMACS Series in Discrete Mathematics and Theoretical Computer Science. AMS.
32. Kautz, H. A., & Selman, B. (1992). Planning as satisfiability. In Proceedings of the 10th European Conference on Artificial Intelligence, pp. 359-363, Vienna, Austria.
33. Kautz, H. A., & Selman, B. (1996). Pushing the envelope: Planning, propositional logic, and stochastic search. In Proceedings, AAAI-96: 13th National Conference on Artificial Intelligence, pp. 1194-1201, Portland, OR.
34. Konuk, H., & Larrabee, T. (1993). Explorations of sequential ATPG using boolean satisfiability. In 11th VLSI Test Symposium, pp. 85-90.
35. Krajíček, J. (2001). On the weak pigeonhole principle. Fandamenta Mathematicae, 170(1-3), 123-140.
36. Krishnamurthy, B. (1985). Short proofs for tricky formulas. Acta Informatica, 22, 253-274.
37. Li, C. M., & Anbulagan (1997). Heuristics based on unit propagation for satisfiability problems. In Proceedings of the 15th International Joint Conference on Artificial Intelligence, pp. 366-371, Nagoya, Japan.
38. Marques-Silva, J. P., & Sakallah, K. A. (1996). GRASP - a new search algorithm for satisfiability. In Proceedings of the International Conference on Computer Aided Design, pp. 220-227, San Jose, CA. ACM/IEEE.
39. Marques-Silva, J. (1998). An overview of backtrack search satisfiability algorithms. In 5th International Symposium on Artificial Intelligence and Mathematics, Fort Lauderdale, Florida.
40. Mézard, M., & Zecchina, R. (2002). Random k-satisfiability problem: From an analytic solution to an efficient algorithm. Physical Review E, 66, 056126.
41. Moskewicz, M. W., Madigan, C. F., Zhao, Y., Zhang, L., & Malik, S. (2001). Chaff: Engineering an efficient SAT solver. In Proceedings of the 38th Design Automation Conference, pp. 530-535, Las Vegas, NV. ACM.
42. Ostrowski, R., Grégoire, E., Mazure, B., & Sais, L. (2002). Recovering and exploiting structural knowledge from cnf formulas. In 8th Principles and Practice of Constraint Programming, Vol. 2470 of Lecture Notes in Computer Science, pp. 185-199, Ithaca, NY. Springer-Verlag.
43. Reda, S., Drechsler, R., & Orailoglu, A. (2002). On the relation between SAT and BDDs for equivalence checking. In Proceedings of the International Symposium on Quality Electronic Design, pp. 394-399, San Jose, CA.
44. Sabharwal, A., Beame, P., & Kautz, H. (2003). Using problem structure for efficient clause learning. In Proceedings of the 6th International Conference on Theory and Applications of Satisfiability Testing, Vol. 2919 of Lecture Notes in Computer Science, pp. 242-256, Portofino, Italy. Springer-Verlag.
45. Shtrichman, O. (2000). Tuning SAT checkers for bounded model checking. In Proceedings of the 12th International Conference on Computer Aided Verification, pp. 480-494, Chicago, IL.
46. Stallman, R., & Sussman, G. J. (1977). Forward reasoning and dependency-directed back-tracking in a system for computer-aided circuit analysis. Artificial Intelligence, 9, 135-196.
47. Velev, M., & Bryant, R. (2001). Effective use of boolean satisfiability procedures in the formal verification of superscalar and vliw microprocessors. In Proceedings of the 38th Design Automation Conference, pp. 226-231.
48. Zhang, H., & Hsiang, J. (1994). Solving open quasigroup problems by propositional reasoning. In Proceedings of the International Computer Symp., Hsinchu, Taiwan.
49. Zhang, H. (1997). SATO: An efficient propositional prover. In Proceedings of the 14th International Conference on Automated Deduction, Vol. 1249 of Lecture Notes in Computer Science, pp. 272-275, Townsville, Australia.
50. Zhang, L., Madigan, C. F., Moskewicz, M. H., & Malik, S. (2001). Efficient conflict driven learning in a boolean satisfiability solver. In Proceedings of the International Conference on Computer Aided Design, pp. 279-285, San Jose, CA. IEEE/ACM.