Extending Fuzzy Constraint Satisfaction Problems
Yasuhiro Sudo*, Masahito Kurihara**, and Tamotsu Mitamura***
*Graduate School of Engineering, Hokkaido University, Kita 13, Nishi 8, Kita-ku, Sapporo 060-8628, Japan
**Graduate School of Information Science and Technology, Hokkaido University, Kita 13, Nishi 8, Kita-ku, Sapporo 060-8628, Japan
***Department of Information Design, Hokkaido Institute of Technology, 4-1, 7-15, Teine-ward, Sapporo 006-8585, Japan
This paper propose a new type of Fuzzy CSP (Constraint Satisfaction Problem) that have a mixture of discrete and continuous domains, and a Spread-Repair algorithm. In traditional CSP and Fuzzy CSP, values for the variables are chosen from the discrete domains. However, this is often inconvenient when one wants to express real world problems. We show that this model, called HDFCSP (Hybrid Domain Fuzzy CSP), can be solved by Spread-Repair, an extension of the well known iterative improvement algorithms. Experimental results on some test problems show that the algorithm actually has an ability of finding partial approximate solutions with high probability in a computation time much shorter than the traditional, discrete-domain FCSP.
-  R. Dechter, “Constraint Processing,” Morgan Kaufmann, 2003.
-  Z. Ruttkay, “Fuzzy constraint satisfaction,” Proceedngs of 3rd IEEE Intern. Conf. on Fuzzy Systems, Vol.3, pp. 1263-1268, 1994.
-  P. Meseguer, and J. Larrosa, “Solving fuzzy constraint satisfaction problems,” Proceedings of 6th IEEE Intern. Conf. on Fuzzy Systems, Vol.3, pp. 1233-1238, 1997.
-  Y. Kanada, “Fuzzy Constraint Satisfaction Using CCM – A Local Information Based Computation Model,” Proceedings of 4th IEEE Intern. Conf. on Fuzzy Systems, Vol.4, pp. 2319-2326, 1995.
-  J. H. Y. Wong, and H. Leung, “Extending GENET to Solve Fuzzy Constraint Satisfaction Problems,” AAAI Constraint Satisfaction Problems, pp. 380-385, 1998.
-  S. Minton, M. D. Johnston, A. B. Philips, and P. Laird, “Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems,” Artificial Intelligence, 58, pp. 161-205, 1992.
-  T. Hogg, B. A. Huberman, and C. Williams, “Phase transitions and search problems,” Artificial Intelligence, Vol.81, Issues1-2, pp. 1-15, 1996.
-  M. V. Marathe, H. Breu, B. Hunt, S. S. Ravi, and D. J. Rosenkrantz, “Simple Heuristics for Unit Disk Graph,” networks, Vol.25, pp. 59-68, 1995.
-  R. P. Brent, “Algorithms for minimization without derivatives,” p. 195, Prentice-Hall, Inc., 1973.
-  C. Badie, and G. Verfile, “OSCAR ou Comment Planifier Intelligemment des Missions Spatiales,” Proceedings of the 9th International Avignon Workshop, 1989.
-  H. M. Adorf, and M. D. Johnston, “A discrete stochastic neural networks algorithm for constraint satisfaction problems,” Proceedings International Joint Conference on Newral Networks, CA, 1990.
-  M. S. Fox, “Constraint-Directed Search: A Case Study of Job-Shop Scheduling,” Morgan Kaufmann, San Mateo, CA, 1987.
-  Y. Takefuji, “Newral Network Parallel Processing,” Kluwer Academic Publishers, 1992.