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A fuzzy model for learning in automata

Published online by Cambridge University Press:  09 March 2009

Ashoke Kumar Datta
Ashoke Kumar Datta
Affiliation:
Electronics and Communication Sciences Unit, Indian Statistical Institute, 203, Barrack pore Trunk Road, Calcutta 700035 (India).
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Summary

A simple general model for learning, using a fuzzy set theoretic approach and fuzzy decision in an automaton which has nonfuzzy input/output, is proposed. The process has been modelled somewhat in the fashion of general biological systems, which may be viewed as a fuzzy decision process where learning consists in taking a tentative action and reinforcing the membership values on the basis of the results of that action. The model is tested on an automaton whose sole purpose is to follow the boundary on an object with which it makes contact during its movements. The automaton is simulated by a computer. it has standard 8–neighbourhood configuration with binary sense capability and three action capabilities. The automaton has been found to learn to take correct action in a large number of possible input situations within only a few thousand moves.

Type
Article
Information
Robotica , Volume 3 , Issue 1 , January 1985 , pp. 39 - 44
Copyright
Copyright © Cambridge University Press 1985

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References

1.Findler, N.V. and Machinsie, W. R., “Computer simulation of a selfpreserving and learning organism” Bull. Math. Biophysics 31, 247–253 (1969).CrossRefGoogle ScholarPubMed
2.Poppeistone, R.J., “Freddy in toyland” Machine Intelligence 4, 455–462 (1969).Google Scholar
3.Nilsson, N.J., “A mobile automation: An application of artificial intelligence technique” IJCAL 10, 509–520 (1969).Google Scholar
4.Eijri, M., Uno, T., Yoda, H., Goto, T. and Takeyama, K., “An intelligent robot with cognition and decision making ability” IJCA 12, 350–358 (1971).Google Scholar
5.Rosen, C.A., “An experimental mobile automation” Tech. Note 39 (Menlo Park, Calif.Art. Intell. Group, Stanford Research Institute, 1970).Google Scholar
6.Hart, P., Nilsson, N. and Robinson, A.E., “A causality representation for enriched robot task domains” Tech. Rept. Proj. 1187 (Menlo Park: Art. Intell. Center, Stanford Research Institute, 1971).Google Scholar
7.Munson, J.H., “Robot planning, execution and monitoring in an uncertain environment” IJCA 12, 338–349 (1971).Google Scholar
8.Dutta, A.K., [1975]: “Learning in a simple robot” Fourth Int. Conf. on Artificial Intelligence (Tblisi, Georgia, 09 1975).Google Scholar
9.Bush, R.R. and Mosteller, F., Stochastic Models for Learning (Wiley, New York, 1951).Google Scholar
10.Estrs, W.K. and Suppes, P., “Foundation of linear models” Studies in Mathematical Learning Theory (Stanford University Press, Stanford, 1959).Google Scholar
11.Sternberg, S. “Stochastic learning theory” Hand book of Mathematical Psychology (Wiley, New York, 1963).Google Scholar
12.Rowar, P.F. and Rosenberg, R., “Description and plan in an interactive robot simulation system” Int. J. Man-Machine Studies 4, 458–488 (1972).Google Scholar
13.Dubois, D. and Prade, H., Fuzzy Sets and Systems: Theory and Application (Academic Press, New York, 1980).Google Scholar