Improving nogood recording using 2SAT
Dept. of Comput. Sci. & Software Eng., Melbourne Univ., Vic., AustraliaDOI: 10.1109/TAI.2003.1250175 Conference: Tools with Artificial Intelligence, 2003. Proceedings. 15th IEEE International Conference on
Source: IEEE Xplore
Nogood recording is a dynamic learning technique widely applied to solve CSP (constraint satisfaction problems). It is highly effective in reducing the search space for SAT (satisfiability) problems. While SAT is NP-complete, the problem restricted to binary clauses (2SAT) is solvable in linear time. We can improve SAT solving by incorporating 2SAT solving techniques. In this paper we investigate extending nogood recording to make use of binary clause resolution. Our experiments show that nogoods generated from binary resolution can significantly reduce the search space, and size of nogoods generated, as well as the search time.
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ABSTRACT: The propositional satisfiability SAT problem is one of the most fundamental problems in computer science. SAT solvers have been successfully applied to a wide range of practical applications, including hardware model checking, software model finding, equivalence checking, and planning, among many others. Empirical research has been very fruitful for the development of efficient methods for SAT problems, such as classical Davis-Putnam method, greedy SAT GSAT method and neural network SAT method. This paper gives a survey about the methods used for solving the SAT problems with an emphasis on surveying the local search algorithms.
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