Generalizing Boolean satisfiability III: Implementation

Journal of Artificial Intelligence Research

Volumn 23, Issue , 2005, Pages 441-531

  Dixon, Heidi E ^a   Ginsberg, Matthew L ^a   Hofer, David ^b   Luks, Eugene M ^b   Parkes, Andrew J ^b  

^a On Time Systems Inc   (United States)

^b UNIVERSITY OF OREGON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMBINATORIAL MATHEMATICS; COMPUTATIONAL METHODS; PROBLEM SOLVING;

BOOLEAN REPRESENTATION; BOOLEAN THEORY; COMPUTATIONAL PERFORMANCE EVALUATION; HIGH-PERFORMANCE SOLVERS;

BOOLEAN ALGEBRA;

EID: 27344435443     PISSN: 10769757     EISSN: 10769757     Source Type: Journal    
DOI: 10.1613/jair.1656     Document Type: Article

References (32)

1. Aho, A. V., Hopcroft, J. E., & Ullman, J. D. (1974). The Design and Analysis of Computer Algorithms. Addison-Wesley.
2. Babai, L., & Moran, S. (1988). Arthur-Merlin games: A randomized proof system, and a hierarchy of complexity classes. J. Comput. System Sci., 36, 254-276.
3. Barth, P. (1995). A Davis-Putnam based enumeration algorithm for linear pseudo-boolean optimization. Tech. rep. MPI-I-95-2-003, Max Planck Institut für Informatik, Saarbrücken, Germany.
4. Bayardo, R. J., & Miranker, D. P. (1996). A complexity analysis of space-bounded learning algorithms for the constraint satisfaction problem. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pp. 298-304.
5. Bayardo, R. J., & Schrag, R. C. (1997). Using CSP look-back techniques to solve real-world SAT instances. In Proceedings of the Fourteenth National Conference on Artificial Intelligence, pp. 203-208.
6. Dixon, H. E., &: Ginsberg, M. L. (2000). Combining satisfiability techniques from AI and OR. Knowledge Engrg. Rev., 15, 31-45.
7. Dixon, H. E., Ginsberg, M. L., Luks, E. M., & Parkes, A. J. (2004a). Generalizing Boolean satisfiability II: Theory. Journal of Artificial Intelligence Research, 22, 481-534.
8. Dixon, H. E., Ginsberg, M. L., & Parkes, A. J. (2004b). Generalizing Boolean satisfiability I: Background and survey of existing work. Journal of Artificial Intelligence Research, 21, 193-243.
9. Furst, M., Hopcroft, J., & Luks, E. (1980). Polynomial time algorithsm for permutation groups. In Proceedings 21st Annual IEEE Symp. on Foundations of Computer Science (FOCS-80), pp. 36-41. IEEE.
10. GAP Group (2004). GAP - Groups, Algorithms and Programming, Version 4-4. http://www.gap-system.org.
11. Ginsberg, M. L. (1993). Dynamic backtracking. Journal of Artificial Intelligence Research, 1, 25-46.
12. Ginsberg, M. L., &: Parkes, A. J. (2000). Search, subsearch and QPROP. In Proceedings of the Seventh International Conference on Principles of Knowledge Representation and Reasoning, Breckenridge, Colorado.
13. Gomes, C. P., & Selman, B. (1997). Problem structure in the presence of perturbations. In Proceedings of the Fourteenth National Conference on Artificial Intelligence, pp. 221-226, Providence, RI.
14. Haken, A. (1985). The intractability of resolution. Theoretical Computer Science, 39, 297-308.
15. Harrison, M. A. (1989). Introduction to Switching and Automata Theory. McGraw-Hill.
16. Heule, M., &: van Maaren, H. (2004). Aligning CNF- and equivalence-reasoning. In The Seventh International Conference on Theory and Applications of Satisfiability Testing, pp. 174-180. Also available as http://www.satisfiability.org/SAT04/programme/72.pdf.
17. Hooker, J. N. (1988). Generalized resolution and cutting planes. Annals of Operations Research, 12, 217-239.
18. Jerrum, M. (1986). A compact representation for permutation groups. J. Algorithms, 7, 60-78.
19. Knuth, D. E. (1991). Notes on efficient representation of permutation groups. Combinatorica, 11, 57-68.
20. Leon, J. (1991). Permutation group algorithms based on partitions I: Theory and algorithms. J. Symbolic Comput., 12, 533-583.
21. Li, C. M. (2000). Integrating equivalency reasoning into Davis-Putnam procedure. In Proceedings of the Seventeenth National Conference on Artificial Intelligence, pp. 291-296.
22. Li, C. M. (2003). Equivalent literal propagation in Davis-Putnam procedure. Discrete App. Math., 130(2), 251-276.
23. Li, C. M., Jurkowiak, B., & Purdom, Jr., P. W. (2002). Integrating symmetry breaking into a DLL procedure. In Fifth International Symposium on the Theory and Applications of Satisfiability Testing (SAT2002), pp. 149-155.
24. Moskewicz, M., Madigan, C., Zhao, Y., Zhang, L., & Malik, S. (2001). Chaff: Engineering an efficient SAT solver. In 39th Design Automation Conference.
25. Pudlak, P. (1997). Lower bounds for resolution and cutting planes proofs and monotone computations. J. Symbolic Logic, 62(3), 981-998.
26. Rotman, J. J. (1994). An Introduction to the Theory of Groups. Springer.
27. Schaefer, T. J. (1978). The complexity of satisfiability problems. In Proceedings of the Tenth Annual ACM Symposium on the Theory of Computing, pp. 216-226.
28. Seress, A. (2003). Permutation Group Algorithms, Vol. 152 of Cambridge Tracts in Mathematics. Cambridge University Press, Cambridge, UK.
29. Sims, C. C. (1970). Computational methods in the study of permutation groups. In Leech, J. (Ed.), Computational Problems in Abstract Algebra, Proc. Conf. Oxford, 1967. Pergamon Press.
30. Tseitin, G. (1970). On the complexity of derivation in propositional calculus. In Slisenko, A. (Ed.), Studies in Constructive Mathematics and Mathematical Logic, Part 2, pp. 466-483. Consultants Bureau.
31. Urquhart, A. (1987). Hard examples for resolution. JACM, 34, 209-219.
32. Zhang, H., & Stickel, M. E. (2000). Implementing the Davis-Putnam method. Journal of Automated Reasoning, 24(1/2), 277-296.