Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Neural networks to guide the selection of heuristics within constraint satisfaction problems
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Learning vector quantization for variable ordering in constraint satisfaction problems
Pattern Recognition Letters
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We proposed a neural network called LPPH-CSP (Lagrange Programming neural network with Polarized High-order connections for Constraint Satisfaction Problem) to solve the CSP. The CSP is a problem to find a variable assignment which satisfies all given constraints. Because the CSP has a well defined representation ability, it can represent many problems in AI compactly. From experimental results of LPPH-CSP and GENET which is a famous CSP solver, we confirmed that our method is as efficient as the GENET. In addition, unlike the other conventional CSP solvers which are discrete-valued methods, our method is a continuous-valued method and it can update all variables simultaneously, while the conventional csp solvers cannot find a solution by updating all variables simultaneously Because of the oscilation of the states. Therefore, we can expect the speed-up of LPPH-CSP if it is implemented by the hardware such as FPGA. In this paper, we extend LPPH-CSP to deal with the linear inequality constraints. By using this type of constraint, we can represent various practical problems more briefly. In this paper, we also define the CSP which has an objective function, and we extend LPPH-CSP to solve this problem. In experiment, we apply our method and OPBDP to the warehouse location problem and compare the effectiveness.