Presentation 1995/8/22
Large-scale Constraint Satisfaction Using Local-information-based Annealing and Its Parallel Processing : An Application of Emergent Computation Model CCM
Yasusi Kanada,
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Abstract(in English) A method for solving large-scale constraint satisfaction problems based on CCM (Chemical Casting Model), which is a model for emergent computation, is proposed in this report. A parallelized version of this method is also shown. Large-scale problems could not be solved using CCM. However, this report shows that, by introducing a method of annealing called FAM (Frustration Accumulation Method) and by adjusting the parameters appropriately, several large-scale graph coloring problems has become solvable with spending the same order of time as GSAT or simulated annealing by sequential processing using CCM. This report also shows that this method can easily be parallelized with restricted amount of mutual exclusion. The performance is almost proportional to the number of processors under certain conditions.
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Keyword(in English) Emergent computation / Constraint satisfaction / Parallel processing / Annealing / Coloring Problem / Graph coloring
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Committee AI
Conference Date 1995/8/22(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Large-scale Constraint Satisfaction Using Local-information-based Annealing and Its Parallel Processing : An Application of Emergent Computation Model CCM
Sub Title (in English)
Keyword(1) Emergent computation
Keyword(2) Constraint satisfaction
Keyword(3) Parallel processing
Keyword(4) Annealing
Keyword(5) Coloring Problem
Keyword(6) Graph coloring
1st Author's Name Yasusi Kanada
1st Author's Affiliation Tsukuba Research Center, Real-World Computing Partnership()
Date 1995/8/22
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Volume (vol) vol.95
Number (no) 211
Page pp.pp.-
#Pages 8
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