Stochastic Problem Solving by Local Computation based on
Tsukuba Research Center,
Real World Computing Partnership
Takezono 1-6-1, Tsukuba, Ibaraki 305, Japan
Advanced Research Laboratory,
Hatoyama, Saitama 350-03, Japan
(C) Copyright 1994 by Yasusi Kanada and IEEE
Created: 6/6/94, Updated: 10/3/95.
This paper is a hypertext version of the following paper:
Y. Kanada, and M. Hirokawa: Stochastic Problem Solving by Local Computation based on Self-organization Paradigm, IEEE 27th Hawaii International Conference on System Sciences, pp. 82-91, 1994.
- Some of the figures in this paper were inaccessible or unavailable
until recently. Sorry. Now all of them are accessible.
- Postscript version is here
We are developing a new problem-solving methodology based on a self-organization paradigm. To realize our future goal of self-organizing computational systems, we have to study computation based on local information and its emergent behavior, which are considered essential in self-organizing systems. This paper presents a stochastic (or nondeterministic) problem solving method using local operations and local evaluation functions. Several constraint satisfaction problems are solved and approximate solutions of several optimization problem are found by this method in polynomial order time in average.
Major features of this method are as follows. Problems can be solved using one or a few simple production rules and evaluation functions, both of which work locally, i.e., on a small number of objects. Local maxima of the sum of evaluation function values can sometimes be avoided. Limit cycles of execution can also be avoided. There are two methods for changing the locality of rules. The efficiency of searches and the possibility of falling into local maxima can be controlled by changing the locality.