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Connectionist expert systems

Author:

Stephen I. GallantAuthors Info & Claims

Communications of the ACM, Volume 31, Issue 2

Pages 152 - 169

https://doi.org/10.1145/42372.42377

Published: 01 February 1988 Publication History

Abstract

Connectionist networks can be used as expert system knowledge bases. Furthermore, such networks can be constructed from training examples by machine learning techniques. This gives a way to automate the generation of expert systems for classification problems.

References

[1]

Anderson, J.A., and Rosenfeld, E., Eds. Neurocomputing, A Reader. MIT Press, Cambridge, Mass., 1988.]]

[2]

Barto, A.G., and Anandan, P. Pattern recognizing stochastic learning automata. IEEE Trans. Syst. Man Cybern. 15, 1985, 360--375.]]

[3]

Bundy, A., Silver, B., and Plummer, D. An analytical comparison of some rule-learning programs. Artif. Intell. 27, 2 (November 1985), 137-'181.]]

Digital Library

[4]

Cheeseman, P.C. A method of computing generalized Bayesian probability values for expert systems. In Proceedings of the 8th International Joint Conference on Artificial Intelligence (Karlsruhe, W. Germany, Aug. 8-12). 1983, pp. 198-202.]]

[5]

Cheeseman, P.C. Learning of expert system data. In Proceedings of the IEEE Workshop on Principles of Knowledge Based Systems (Denver, Colo., Dec. 3-4). IEEE Press, New York, 1984, pp. 115-.122.]]

[6]

ft. Bavis, R., and Lenat, D.B. Knowledge-Based Systems in Artificial Intelligence. McGraw-Hill, New York, 1980.]]

Digital Library

[7]

Duda, R.O., and Shortliffe, E.H. Expert systems research. Science 220, 4594 (Apr. 15, 1983), 261-268.]]

[8]

Fisher, R.A. The use of multiple measurements in taxonomic problems. Ann. Eugen. 7, (1936) Part II, 179-188. {Also in Contributions to Mathematical Statistics, Wiley, New York, 1950.)]]

[9]

Fukushima, K, Miyake, S., and {to, T. Neocognitron: A neural network model for a mechanism of visual pattern recognition. IEEE Trans. Syst. Man Cybern. SMC-13, 5 (Sept.-Oct. 1983), 826-834.]]

[10]

Gallant, S.I. Automatic generation of expert systems fi'om examples. In Proceedings of the 2nd International Conference on Artificial Intelligence Applications (Miami Beach, FI., Dec. 11-13). IEEE Press, New York, 1985, pp. 313-319.]]

[11]

Gallant, S.I. Matrix controlled expert system producible from examples. U.S. Patent Pending 707,458, 1985.]]

[12]

Gallant, S.{. Brittleness and machine learning. In International Meeting on Advances in Learning sponsored by Association Franqaise pour l'Apprentissage Symbolique Automatique, CNRS, Paris, France. (Les Arcs, France, July 28-Aug. 1, 1986).]]

[13]

Gallant, S.I. Optimal linear discriminants. In Proceedings of the 8th International Conference on Pattern Recognition (Paris, France, Oct. 28-31}. IEEE Press, New York, 1986, pp. 849-852.]]

[14]

Gallant, S.I. Automated generation of expert systems for problems involving noise and redundancy. In AAAI Workshop on Uncertainty in Artificial Intelligence sponsored by AAAI. (Seattle, Wash., July 10-12, 1987), pp. 212-221.]]

[15]

Gallant, S.I. Bayesian assessment of a connectionist model for fault detection. Tech. Rep. NU-CCS-87-25, College of Computer Science, Northeastern Univ., Boston, Mass., 1987.]]

[16]

Gallant, S.I., and Balachandra, R. Using automated techniques to generate an expert system for R/D project monitoring. In International Conference on Economics and Artificial Intelligence sponsored by AFCET, Paris, France. (Aix-en-Provence, France, Sept. 2-4, 1986), pp. 87-92.]]

[17]

Gallant, S.I., and Smith, D. Random cells: An idea whose time has come and gone ... and come again? In IEEE Internatior~'al Conference on Neural Networks (San Diego, Calif., June). {EEE Press, New York, 1987, pp. 21-24.]]

[18]

Grossberg, S. Studies of Mind and Brain. Reidel, Hingham, Mass.]]

[19]

Hinton, G.E., and Anderson, J.A., Eds. Parallel Models of Associative Memory. Erlbaum, Hillsdale, N.J., 1981.]]

Digital Library

[20]

Hopfield, J.J. Neural networks and physical systems with emergent collective computational abilities. In Proceedings of the National Academy of Sciences USA. National Academy of Sciences, Washington, D.C., 1982, vol. 79, 2554-2558.]]

[21]

Kim, J.H., and Pearl, J. CONVINCE: A conversational inference consolidation engine. IEEE Trans. Syst. Man Cybern. SMC-17, 2 (Mar.- Apr. 1987), 120-132.]]

Digital Library

[22]

McClelland, J.L., and Rumelhart, D.E., Eds. Parallel Distributed Processing: Explorations in the Microstructures of Cognition. Vol. 2. MIT Press, Cambridge, Mass. (1986).]]

Digital Library

[23]

McCulloch, W.S. and Pitts, W.H. A logical calculus of the ideas imminent in nervous activity. Bull. Math. Biophys. 5 (1943), 115-133. (Reprinted: McCulloch, W.S. Embodiments of Mind. MIT Press, Cambridge, Mass., 1965.)]]

[24]

McDermott, J. RI: The formative years. AI Meg. 2, 2 (1981), 21-29.]]

[25]

Michalski, R.S., Carbonell. J.G., and Mitchell, T.M. Machine Learning. Tioga, Pale Alto, Calif., 1983.]]

[26]

Michalski, R.S., Carbonell, J.G., Mitchell, T.M. Machine Learning. Vol. 2. Kaufmann, Los Altos, Calif., 1986.]]

[27]

Minsky, M., and Papert, S. Perceptrons: An Introduction to Computational Geometry. MIT Press, Cambridge, Mass., 1969.]]

Digital Library

[28]

Nilsson, N.J. Learning Machines. McGraw-Hill, New York, 1965.]]

[29]

Pearl, J. How to do with probabilities what people say you can't. In Proceedings of the 2nd International Conference on Artificial Intelligence Applications (Miami Beach, Fla., Dec. 11-13). IEEE Press, New York, 1985, pp. 6-12.]]

[30]

Pearl, J. Fusion, propagation, and structuring in belief networks. Artif. InteU. 29, 3 (Sept. 1986), 241-288.]]

Digital Library

[31]

Pearl, J. The logic of representing dependencies by directed graphs. In AAAI-87 sponsored by AAAI, Menlo Park, Calif. (Seattle, Wash. July 13-17, 1987), pp. 374-379.]]

[32]

Quinlan, J.R. Learning efficient classification procedures and their application to chess end games. In Machine Learning, Eds. R.S. Michalski, J.G. Carbonell, and T.M. Mitchell, Tioga, Pale Alto, Calif., 1983.]]

[33]

Rosenblatt, F. Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan, Washington, D.C., 1961.]]

[34]

Rumelhart, D.E., and McClelland, I.L., Eds. Parallel Distributed Processing: Explorations in the Microstructures of Cognition. Vol. 1. MIT Press, Cambridge, Mass.]]

Digital Library

[35]

Werbos, P.J. Beyond regression: New tools for prediction and analysis in the behavioral sciences. Ph.D. thesis, Dept. of Applied Mathematics, Harvard Univ., Cambridge, Mass., 1974.]]

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Published In

cover image Communications of the ACM

Communications of the ACM Volume 31, Issue 2

Feb. 1988

118 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/42372

Editor:
Peter J. Denning
NASA Ames Research Center, Moffett Field, CA

Issue’s Table of Contents

Copyright © 1988 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 01 February 1988

Published in CACM Volume 31, Issue 2

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