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A quantitative solution to Constraint Satisfaction Problem (CSP)

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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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Author information

Author notes

  1. Jun Gu

    Present address: Dept. of Computer Science, Univ. of Science and Technology of China, P. R. China

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Calgary, T2N 1N4, Calgary, Alberta, Canada

    Jun Gu, Xiaofei Huang & Bin Du

Authors
  1. Jun Gu
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  2. Xiaofei Huang
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  3. Bin Du
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Additional information

This work was supported in part by 1987 and 1988 ACM/IEEE Academic Scholarship Awards and is presently supported in part by NSERC Strategic Grant MEF0045793 and NSERC Research Grant OGP0046423.

Xiaofei Huang’s address is unknown. This work was present in part in Technical Report UCECE-TR-91-002.

Jun Gu, Ph. D: He is a Professor of Electrical and Computer Engineering at the University of Calgary, Canada, and a Professor of Computer Science at the University of Science and Technology of China (USTC). He received B. S. degree in electrical engineering from USTC in 1982 and Ph. D degree in computer science from the University of Utah in 1989. His present research interest includes operations research and combinatorial optimization, communication systems, computer architecture and parallel processing, and structured techniques for CMOS, GaAs, and MCM system design. In 1987 he gave several discrete and continuous local search algorithms for the satisfiability problem.

Xiaofei Huang, Ph. D: He is working in Omni Vision, California, U. S. He received his Ph. D degree from Tsinghua University, Beijing, in 1990. He was a postdoctoral researcher of the University of Calgary during 1991-1993 and was a visiting researcher at the Institute of Roboties and Intelligent systems, USC, 1994. His present interests are image processing, logic reasoning, machine vision, and neural networks.

Bin Du: He received the B. S. degree in Scientific Instrumentation in 1984 and the M. S. degree in Mechanics in 1987, all from the Zhejiang University, China. He received the M.S. degree in Electrical and Computer Engineering from the University of Calgary, Canada, in 1994. Presently he is a Ph. D candidate in the Department of Electrical and Computer Engineering at the University of Calgary. His current research interest includes instrumentation, VLSI testing, image processing, and optimization techniques.

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Gu, J., Huang, X. & Du, B. A quantitative solution to Constraint Satisfaction Problem (CSP). New Gener Comput 13, 99–115 (1994). https://doi.org/10.1007/BF03038310

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  • DOI: https://doi.org/10.1007/BF03038310

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