Towards fewer parameters for SAT clause weighting algorithms

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2557, Issue , 2002, Pages 569-578

  Thornton, John ^a   Pullan, Wayne ^a   Terry, Justin ^a  

^a GRIFFITH UNIVERSITY   (Australia)

Author keywords

Constraints; Search

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; LAGRANGE MULTIPLIERS; PROBLEM SOLVING; STOCHASTIC SYSTEMS; TABU SEARCH;

BENCH-MARK PROBLEMS; CONSTRAINTS; FINE TUNING; PARAMETER SETTING; RUN-TIME PERFORMANCE; SAT PROBLEMS; SEARCH; STOCHASTIC TECHNIQUES;

PARAMETER ESTIMATION;

EID: 84934269088     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/3-540-36187-1_50     Document Type: Conference Paper

References (11)

1. J. Frank. Learning short term weights for GSAT. In "Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI-97), pages 384-389, 1997.
2. I. Gent and T. Walsh. Towards an understanding of hill-climbing procedures for SAT. In Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI-93), pages 28-33, 1993.
3. F. Glover. Tabu search: Part 1. ORSA Journal on Computing, 1(3):190-206, 1989.
4. B. Mazure, S. Lakhdar, and E. Gregoire.Tabu search forSAT. In Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI-97), pages 281-285, 1997.
5. P. Morris. The Breakout method for escaping local minima. In Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI-93), pages 40-45, 1993.
6. D. Schuurmans and F. Southey. Local search characteristics of incomplete SAT procedures. In Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-00), pages 297-302, 2000.
7. D. Schuurmans, F. Southey, and R. Holte. The exponentiated subgradient algorithm for heuristic Boolean programming. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), pages 334-341, 2001.
8. B. Selman and H. Kautz. Domain-independent extensions to GSAT: Solving large structured satisfiability problems. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93), pages 290-295, 1993.
9. Y. Shang and B.Wah. A discrete Lagrangian-based global search method for solving satisfiability problems. J. Global Optimization, 12:61-99, 1998.
10. Z.Wu and B.Wah. Trap escaping strategies in discrete Lagrangian methods for solving hard satisfiability and maximum satisfiability problems. In Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), pages 673-678, 1999.
11. Z. Wu and B. Wah. An efficient global-search strategy in discrete Lagrangian methods for solving hard satisfiability problems. In Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-00), pages 310-315, 2000.