Solving Constraint Satisfaction Problew by Using Dynamics Which Can Search State-Space Globally
| Project/Area Number |
14580383
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
計算機科学
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| Research Institution | Kyushu Institute Of Technology |
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Principal Investigator |
NAGAMATU Masahiro Kyushu Institute Of Technology, Graduate School of Life Science and Systems Engineering, Professor, 大学院・生命体工学研究科, 教授 (70117307)
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| Project Period (FY) |
2002 – 2003
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| Project Status |
Completed (Fiscal Year 2003)
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| Budget Amount *help |
¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 2003: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2002: ¥900,000 (Direct Cost: ¥900,000)
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| Keywords | satisfiability problem / constraint satisfaction problem / neural network / Lagrangian method / global state-space search |
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| Research Abstract |
For the satisfiability problem of propositional calculus (SAT), we have proposed a neural network dynamics called LPPH which is based on Lagrangian method. From experimental results it is known that this dynamics can find a solution more effectively than already proposed method. In this project, we investigate the following :
1)The LPPH is extended from a solver of the SAT to a solver of the more general constraint satisfaction problems. By experiments we show that this extension is effective. We also show the LPPH is suitable for massively parallel processing and hardware implementation.
2)To speedup the LPPH we proposed the following method : (1)reduction of the amount of computation based on the fact that at each time step only small number of variables change their values, and (2)giving different degree of importance for each type of constraint.
3)Application for automatic layout of UML class diagram.
4)Proposal of parallel execution of the LPPH.
5)Implementation of the LPPH by interconnection networks or electronic circuits.
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Report
(3 results)
Research Products
(16 results)
