Using Randomization and Learning to Solve Hard Real-World Instances of Satisfiability-Reference-Cited by-同舟云学术

Using Randomization and Learning to Solve Hard Real-World Instances of Satisfiability

Published:2000 Issue: Volume: Page:489-494
ISSN:0302-9743
Container-title:Principles and Practice of Constraint Programming – CP 2000
language:
Short-container-title:

Author:

Baptista Luís,Marques-Silva João

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/3-540-45349-0_36

Reference8 articles.

1. L. Baptista and J. P. Marques-Silva. The interplay of randomization and learning on real-world instances of satisfiability. In AAAI Workshop on Leveraging Probability and Uncertatinty in Computation, July 2000.

2. R. Bayardo Jr. and R. Schrag. Using CSP look-back techniques to solve real-world SAT instances. In Proceedings of the National Conference on Artificial Intelligence, 1997.

3. C. P. Gomes and B. Selman. Algorithm portfolio design: Theory vs. practice. In Proceedings of the Thirteenth Conference On Uncertainty in Artificial Intelligence, 1997.

4. C. P. Gomes, B. Selman, and H. Kautz. Boosting combinatorial search through randomization. In Proceedings of the National Conference on Artificial Intelligence, July 1998.

5. C. M. Li and Anbulagan. Look-ahead versus look-back for satisfiability problems. In Proceedings of International Conference on Principles and Practice of Constraint Programming, 1997.

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