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Sequential learning of automata from input–output behaviour

Published online by Cambridge University Press:  09 March 2009

P. Luneau ,
M. Richetin  and
C. Cayla
P. Luneau
Affiliation:
Electronics Laboratory, ERA 90 of the CNRS, University of Clermont II, B.P. 45, 63170 Autière (France)
M. Richetin
Affiliation:
Electronics Laboratory, ERA 90 of the CNRS, University of Clermont II, B.P. 45, 63170 Autière (France)
C. Cayla
Affiliation:
Electronics Laboratory, ERA 90 of the CNRS, University of Clermont II, B.P. 45, 63170 Autière (France)
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Summary

A learning algorithm for the inference of sequential machines from input/output behaviour is developed. The construction of the models is sequentially achieved by processing input/output (i/o) sequences, and through an induction-contradiction-discrimination scheme. The various states are discriminated after the discovery of contradictory i/o sequences. The properties of the inferred machines and some reported experiments indicate the efficiency of the learning concept of contradiction for this application. Possible application in Robotics for the learning of assembling models is also mentioned.

Information

Type
Article
Information
Robotica , Volume 1 , Issue 3 , July 1983 , pp. 151 - 159
Copyright
Copyright © Cambridge University Press 1983

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References

1.Fu, K.S., Syntactic Pattern Recognition and Applications (Prentice Hall, Englewood Cliffs, N.J., 1982).Google Scholar
2.Gonzalez, R.C. & Thomason, M.G., Syntactic Pattern Recognition; an Introduction, (Addison-Wesley, Reading, Massachusetts, 1978).Google Scholar
3.Chow, T.S., “Testing software design modeled by finite state machines ” IEEE Trans, on Software Engineering SE-4, No. 3, 178–187 (05, 1978).CrossRefGoogle Scholar
4.Koenig, E.C., “Associating current knowledge with that of past experience based on knowledge about automata” Kybernetes 11, No. 3, 211–217 (1982).CrossRefGoogle Scholar
5.Biermann, A.W., “On the inference of turing machine from sample computations” Artificial Intelligence 3, 181–198 (1972).CrossRefGoogle Scholar
6.Biermann, A.W. & Feldmann, J.A., “On the synthesis of finite state machines from samples of their behaviour” IEEE Trans, on Computers C21, No. 6, 592–597 (06, 1972).CrossRefGoogle Scholar
7.Booth, T.L., Sequential Machines and Automata Theory (Wiley, New York, 1968).Google Scholar
8.Fu, K.S. & Booth, T.L., “Grammatical inference: introduction and survey. Part I IEEE Trans, on Syst., Man and Cybern. SMC-5, No. 1, 95–111 (01, 1975).CrossRefGoogle Scholar
9.Miclet, L., “Regular inference with a tail clustering method” IEEE Trans, on Syst. Man and Cybern. SMC-10, No. 11, 737–743 (11, 1980).CrossRefGoogle Scholar
10.Veelenturf, L.P.J., “Inference of sequential machines from sample computations” IEEE Trans, on Computers C27, No. 2, 167–170 (02, 1978).CrossRefGoogle Scholar
11.Veelenturf, L.P.J., “An automata theoretical approach to developing learning neural networks” Cybernetics and Systems 12, 179–202 (1981).CrossRefGoogle Scholar
12.Vernadat, F., Richetin, M. & Rives, G., “Regular grammatical inference by a successor method,” 6th International Conference on Pattern Recognition, Munich (10, 1982).Google Scholar
13.Aho, A. V. & Ullman, J.D., The Theory of Parsing, Translation and Compiling (Prentice-Hall, Englewood Cliffs, N.J., 1972).Google Scholar
14.Dufay, B., & Latombe, J.C., “Robot Programming by inductive inference ” 8th IJCAI, Karlsruhe (08, 1983).Google Scholar