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Learning design concepts using machine learning techniques

Published online by Cambridge University Press:  27 February 2009

Mary Lou Maher  and
Heng Li
Mary Lou Maher
Affiliation:
Department of Architectural and Design Science, The University of Sydney, Sydney, NSW 2006, Australia
Heng Li
Affiliation:
Department of Architectural and Design Science, The University of Sydney, Sydney, NSW 2006, Australia
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Abstract

The use of machine learning techniques requires the formulation of a learning problem in a particular domain. The application of machine learning techniques in a design domain requires the consideration of the representation of the learned design knowledge, that is, a target representation, as well as the content and form of the training data, or design examples. This paper examines the use of a target representation of design concepts and the application, adaptation, or generation of machine learning techniques to generate design concepts from design examples. The examples are taken from the domain of bridge design. The primary machine learning paradigm considered is concept formation.

Keywords

Machine LearningDesignConcept FormationEmpirical NetworkBridge Design
Type
Articles
Information
AI EDAM , Volume 8 , Issue 2 , Spring 1994 , pp. 95 - 111
Copyright
Copyright © Cambridge University Press 1994

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References

REFERENCES

Alem, L., & Maher, M.L. (1991). Using conceptual clustering to learn about function, structure and behavior in design. In Knowledge Modelling and Expertise Transfer, (Herin-Aime, D., Dieng, R., Regourd, J.P., and Angoujard, J.P., Eds.), pp. 163–177. IOS, Amsterdam.Google Scholar
Burford, R.L. (1968). Statistics, a Computer Approach. Merrill, Charles E., Columbus, OH.Google Scholar
Coyne, R.D., Rosenman, M.A., Radford, A.D., Balachandran, M., & Gero, J.S. (1990). Knowledge-Based Design Systems. Addison-Wesley, Reading, MA.Google Scholar
Finger, S., & Dixon, J.R. (1989). A review of research in mechanical engineering design. Part I: Descriptive, prescriptive, and computer-based models of design processes. Res. Eng. Design 1(1), 51–67.CrossRefGoogle Scholar
Fisher, D. (1987). Knowledge acquisition via incremental conceptual clustering. Machine Learning 2, 139–172.CrossRefGoogle Scholar
Gennari, J.H., Langley, P., & Fisher, D. (1989). Models of incremental concept formation. Artificial Intelligence 40(1–3), 11–61.CrossRefGoogle Scholar
Greer, A. (1979). Statistics for Engineering. Thornes, Cheltenham, US.Google Scholar
Katz, M.J. (1984). Templets and the Explanation of Complex Patterns. University Press, Cambridge.Google Scholar
Langley, P., Simon, H.A., & Bradshaw, G.L. (1987). Heuristics for empirical discovery. In Computational Models of Learning (Leonard, B., Ed.), pp. 21–54. Springer-Verlag, Berlin.CrossRefGoogle Scholar
Maher, M.L. (1990). Process models for design synthesis. AI Magazine, 11(4), 49–58.Google Scholar
McCarthy, J., & Hayes, P.J. (1969). Some philosophical problems from the standpoint of artificial intelligence. In Machine Intelligence 4, (Meltzer, B., and Mitchie, D., Eds.), pp. 463–502. Edinburgh.Google Scholar
Michalski, R.S., & Stepp, R. (1983). Learning from observation: Conceptual clustering. In Machine Learning: An Artificial Intelligence Approach (Michalski, R.S., Carbonell, J.G., and Mitchell, T.M., Eds.), pp. 163–177. Morgan Kaufmann, San Mateo, CA.CrossRefGoogle Scholar
Montanari, U. (1974). Networks of constraints, fundamental properties and applications to picture processing. Information Sci. 7, 95–132.CrossRefGoogle Scholar
Rao, R.B., Lu, S. C-Y., & Stepp, R.E. (1991). Knowledge-based equation discovery in engineering domains. In Machine Learning, Proc. Eight Int. Workshop (ML91), 630–634.CrossRefGoogle Scholar
Reich, Y. (1990). Converging to “ideal” design knowledge by learning. In Proc. First Int. Workshop Formal Methods Eng. Design, 330–349.Google Scholar
Stepp, R.E. (1987). Machine learning from structured objects. Proc. Fourth Int. Workshop Machine Learning, 353–363.CrossRefGoogle Scholar
Thagard, P. (1988). Computational Philosophy of Science. MIT Press, Cambridge, MA.CrossRefGoogle Scholar
Woods, W.A. (1975). What's in a link? Foundations for semantic networks. In Representation and Understanding (Bobrow, D., and Collins, A., Eds.), pp. 35–82. Academic Press, New York.CrossRefGoogle Scholar