A quantitative solution to Constraint Satisfaction Problem (CSP) |
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Authors: | Jun Gu Xiaofei Huang Bin Du |
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Affiliation: | 1. Department of Electrical and Computer Engineering, University of Calgary, T2N 1N4, Calgary, Alberta, Canada
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Abstract: | Constraint Satisfaction Problem (CSP) is an important problem in artificial intelligence and operations research. Many practical problems can be formulated as CSP, i.e., finding a consistent value assignment to variables subject to a set of constraints. In this paper, we give a quantitative approach to solve the CSPs which deals uniformly with binary constraints as well as high order,k-ary (k ≥ 2) constraints. In this quantitative approach, using variable transformation and constraint transformation, a CSP is transformed into a satisfiability (SAT) problem. The SAT problem is then solved within a continuous search space. We will evaluate the performance of this method based on randomly generated SAT problem instances and regularly generatedk-ary (k ≥ 2) CSP problem instances. |
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