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In Proceedings of the 9th International Conference on Theory and Applications of Satisfiability Testing, SAT-2006, Seattle, USA, pages 240–251. Springer LNCS 4121, 2006. 7. M. Bonet, J. Levy, and F. Many` a. Resolution for Max-SAT. Artificial Intelligence, 2007. 001. 8. B. Cha, K. Iwama, Y. Kambayashi, and S. Miyazaki. Local search algorithms for partial MAXSAT. In Proceedings of the 14th National Conference on Artificial Intelligence, AAAI’97, Providence/RI, USA, pages 263–268. AAAI Press, 1997.
Improved exact solver for weighted Max-SAT. In Proceedings of the 8th International Conference on Theory and Applications of Satisfiability Testing, SAT-2005, St. Andrews, Scotland, pages 371–377. Springer LNCS 3569, 2005. 2. J. Argelich and F. Many` a. Solving over-constrained problems with SAT technology. In Proceedings of the 8th International Conference on Theory and Applications of Satisfiability Testing, SAT-2005, St. Andrews, Scotland, pages 1–15. Springer LNCS 3569, 2005. Partial Max-SAT Solvers with Clause Learning 39 3.
22–27, 2007. c Springer-Verlag Berlin Heidelberg 2007 Breaking Symmetries in SAT Matrix Models 23 However, this is not the case for preprocessing techniques in general. Only speciﬁc techniques applied to speciﬁc problems have been shown to be eﬀective. On the other hand, mainly due to the eﬀectiveness of SAT solvers learning techniques, modelling has not been much developed in SAT, at least when compared with other areas such as CP. Jointly with dynamic heuristics, learning is able to extend the formula in such a way that strategic resolution steps are performed.
Compiler techniques by Bary W Pollack