Goodness-of-fit techniques
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
Domain-independent extensions to GSAT: solving large structured satisfiability problems
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Theoretical analysis of Davis-Putnam procedure and propositional satisfiability
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Performance test of local search algorithms using new types of random CNF formulas
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Clustering at the phase transition
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Optimizing with minimum satisfiability
Artificial Intelligence
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The 3-satisfiability problem (3SAT) has had a central role in the study of complexity. It was recently found that 3SAT instances transition sharply from satisfiable to nonsatisfiable as the ratio of clauses to variables increases. Because this phase transition is so sharp, the ratio - an order parameter - can be used to predict satisfiability. This paper describes a second order parameter for 3SAT. Like the classical order parameter, it can be computed in linear time, but it analyzes the structure of the problem instance more deeply. We present an analytical method for using this new order parameter in conjunction with the classical one to enhance satisfiability prediction accuracy. The assumptions of the method are verified by rigorous statistical testing. The method significantly increases the satisfiability prediction accuracy over using the classical order parameter alone. Hardness - i.e. how long it takes to determine satisfiability - results for one complete and one incomplete algorithm from the literature are also presented as a function of the two order parameters. The importance of new order parameters lies in the fact that they refine the locating of satisfiable vs. nonsatisfiable and hard vs. easy formulas in the space of all problem instances by adding a new dimension in the analysis.