Presentation 2005/3/22
Lagrange Neural Network for Solving Constraint Satisfaction Problem
Takahiro NAKANO, Masahiro NAGAMATU,
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Abstract(in English) The constraint satisfaction problem (CSP) is a combinatorial problem to find a solution which satisfies all given constraints. The CSP can represent various problems which appear in the fields of Artificial Intelligence. In this paper, we propose a neural network called LPPH-CSP to solve the CSP. Every equilibrium point of this neural network is a solution of the CSP, and vice versa. Thus the LPPH-CSP is not trapped by any point which is not a solution of the CSP. We compare the LPPH-CSP with the GENET which is a famous CSP-solver. Experimental results show that our method is as efficient as the GENET. However our neural network can update all neuron simultaneously for solving the CSP. In contrast, the GENET must update variables sequentially. We consider that this is an advantage for the VLSI implementation.
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Keyword(in English) Neural Network / constraint satisfaction problem / local search / Lagrangian method
Paper # NC2004-178
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Committee NC
Conference Date 2005/3/22(1days)
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Registration To Neurocomputing (NC)
Language ENG
Title (in Japanese) (See Japanese page)
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Title (in English) Lagrange Neural Network for Solving Constraint Satisfaction Problem
Sub Title (in English)
Keyword(1) Neural Network
Keyword(2) constraint satisfaction problem
Keyword(3) local search
Keyword(4) Lagrangian method
1st Author's Name Takahiro NAKANO
1st Author's Affiliation Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology()
2nd Author's Name Masahiro NAGAMATU
2nd Author's Affiliation Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology
Date 2005/3/22
Paper # NC2004-178
Volume (vol) vol.104
Number (no) 759
Page pp.pp.-
#Pages 6
Date of Issue