Kanada, Y., not yet published, 1996.

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Abstract:
A method for solving large-scale constraint satisfaction problems using
an annealed symmetrically-connected neural network, which is called
DSN-FAM, is proposed in the present paper. Some conventional methods,
such as Hopfield networks, often fail to find a solution. Some others,
such as Boltzmann Machines, take too much time. These difficulties are
solved by a type of annealing technique, which we call the frustration
accumulation method (FAM). DSN-FAM works only with local information,
and no global functions or global parameters such as a temperature are
used. DSN-FAM thus works autonomously. That is, no external control is
necessary while operating. Experiments show that this method does not
fail to find a solution and the execution time is less than one tenth of
Boltzmann Machines. The performance can be easily and almost linearly
improved by parallel processing using tens of processors.

Introduction to this research theme:
CCM: Chemical-Computation Model

Keywords: CCM, Constraint satisfaction problem, Neural networks, Randomized computation, Randomized problem solving, Annealing, Rule-based computation, Rule-based problem solving, FAM, Frustration accumulation method

Kanada, Y., Unpublished, 1996.

[ 日本語のページ ]

[ Paper PDF file ] [ Paper postscript file ]

[ **Several constraint satisfaction problem demos in Java** (You can change catalysts, rules, and frustration.) ]

Abstract: A method for solving large-scale constraint satisfaction problems is proposed in the present paper. This method is stochastic (or randomized) and uses local information only, i.e., no global plan is expressed in the program and the computation refer to no global information. This method uses CCM (Chemical Casting Model) as a basis, which is a model for emergent computation proposed by the author. The original CCM-based method minimizes the number of constraint violations not directly but throught optimization of local functions, which are called LODs (local order degrees). This method sometimes falls into a "local maximum." This difficulty is solved by a type of annealing, which we call the frustration accumulation method (FAM). FAM also works only with local information. No global functions is used in FAM, No global parameters such as temperature are used, and global control is thus unnecessary. Experiments show that the performance of this method is not very sensitive to parameter values. This means that parameter tuning is easy. In several problems, the performance is comparable to conventional simulated annealing or GSAT, which are based on global evaluation functions. Because of the nonexistence of global information reference, CCM with FAM can be parallelized very easily. Thus, the performance is improved and is almost linear in certain cases.

Introduction to this research theme:
CCM: Chemical-Computation Model

Keywords: CCM, Constraint satisfaction problem, Parallel Processing, Randomized computation, Randomized problem solving, Annealing, Rule-based computation, Rule-based problem solving, Function optimization, Local information, Localized computation, Local evaluation function, FAM, Frustration accumulation method