Solving fuzzy constraint satisfaction problems with fuzzy GENET

Constraint satisfaction is well known to be applicable in modeling AI problems. Despite their extensive literature, the framework is sometimes inflexible and the results are not very satisfactory when applied to real-life problems. With the incorporation of the theory of fuzzy sets, fuzzy constraint satisfaction problems (FCSP's) have been exploited. FCSP's model real-life problems better by allowing both full and partial satisfaction of individual constraints. GENET, which has been shown to be efficient and effective in solving certain traditional CSPs, has been extended to handle FCSPs. Through transforming FCSPs into 0-1 integer programming problems, Wong and Leung (1998) displayed the equivalence between the underlying working mechanism of fuzzy GENET and the discrete Lagrangian method. We focus on the performance of fuzzy GENET in attacking large-scale and real-life over-constrained problems. An efficient simulator of fuzzy GENET for single-processor machines is implemented. Benchmarking results confirm its feasibility, flexibility, and superb efficiency in tackling both CSPs and FCSPs.