Old resolution meets modern SLS

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2005, Pages 354-359

  Anbulagan, ^a   Pham, Due Nghia ^b   Slaney, John ^a,c   Sattar, Abdul ^b  

^a CSIRO   (Australia)

^b GRIFFITH UNIVERSITY   (Australia)

^c AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS; MARKOV PROCESSES;

CSP DOMAINS; LEARNING PROBLEMS; STOCHASTIC LOCAL SEARCH (SLS);

RANDOM PROCESSES;

EID: 29344433076     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Billionnet, A., and Sutter, A. 1992. An efficient algorithm for 3-satisfiability problem. Operations Research Letters 12:29-36.
2. Cha, B., and Iwama, K. 1996. Adding new clauses for faster local search. In Proceedings of the 13th AAAI, 332-337.
3. Davis, M., and Putnam, H. 1960. A computing procedure for quantification theory. Journal of the ACM 7:201-215.
4. Fang, H., and Ruml, W. 2004. Complete local search for propositional satisfiability. In Proceedings of the 19th AAAI, 161-166.
5. Frank, J. 1997. Learning short-term clause weights for GSAT. In Proceedings of the 15th IJCAI, 384-389.
6. Fujita, M.; Slaney, J.; and Bennett, F. 1993. Automatic generation of some results in finite algebra. In Proceedings of the 13th IJCAI, 52-57.
7. Gent, I. P., and Walsh, T. 1995. Unsatisfied variables in local search. In Proceedings of AISB'95, Hybrid Problems. Hybrid Solutions, 73-85.
8. Hoos, H. H., and Stulze, T. 2005. Stochastic Local Search. Cambridge, Massachusetts: Morgan Kaufmann.
9. Hoos, H. H. 1999. On the run-time behaviour of stochastic local search algorithms for SAT. In Proceedings of the 16th AAAI, 661-666.
10. Hoos, H. H. 2002. An adaptive noise mechanism for Walk-SAT. In Proceedings of the 18th AAAI, 655-660.
11. Hutter, F.; Tompkins, D. A. D.; and Hoos, H. H. 2002. Scaling and probabilistic smoothing: Efficient dynamic local search for SAT. In Proceedings of the 8th CP, 233-248.
12. Li, C. M., and Anbulagan. 1997. Look-ahead versus look-back for satisfiability problems. In Proceedings of the 3rd CP, 341-355.
13. McAllester, D. A.; Selman, B.; and Kautz, H. A. 1997. Evidence for invariants in local search. In Proceedings of the 14th AAAI, 321-326.
14. Morris, P. 1993. The breakout method for escaping from local minima. In Proceedings of the 11th AAAI, 40-45.
15. Quine, W. V. 1955. A way to simplify truth functions. American Mathematical Monthly 62:627-631.
16. Robinson, J. A. 1965. A machine-oriented logic based on the resolution principle. Journal of the ACM 12:23-41.
17. Schuurmans, D., and Southey, F. 2000. Local search characteristics of incomplete SAT procedures. In Proceedings of the 17th AAAI, 297-302.
18. Schuurmans, D.; Southey, F.; and Holte, R. C. 2001. The exponentiated subgradient algorithm for heuristic boolean programming. In Proceedings of the 17th IJCAI, 334-341.
19. Selman, B., and Kautz, H. A. 1993. Domain-independent extensions to GSAT: Solving large structured satisfiability problems. In Proceedings of the 13th IJCAI, 290-295.
20. Selman, B.; Kautz, H. A.; and Cohen, B. 1994. Noise strategies for improving local search. In Proceedings of the 12th AAAI, 337-343.
21. Selman, B.; Levesque, H.; and Mitchell, D. 1992. A new method for solving hard satisfiability problems. In Proceedings of the 10th AAAI, 440-446.
22. Thornton, J.; Pham, D. N.; Bain, S.; and Ferreira Jr., V. 2004. Additive versus multiplicative clause weighting for SAT. In Proceedings of the 19th AAAI, 191-196.
23. Tompkins, D. A., and Hoos, H. H. 2004. Warped landscapes and random acts of SAT solving. In Proceedings of the 8th International Symposium on Artificial Intelligence and Mathematics.
24. Wu, Z., and Wah, B. W. 2000. An efficient global-search strategy, in discrete lagrangian methods for solving hard satisfiability problems. In Proceedings of the 17th AAAI, 310-315.