Skip to main content Accessibility help
×
Home
Hostname: page-component-559fc8cf4f-67gxp Total loading time: 0.238 Render date: 2021-03-05T08:23:29.439Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": false, "newCiteModal": false, "newCitedByModal": true }

Article contents

Learning design concepts using machine learning techniques

Published online by Cambridge University Press:  27 February 2009

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

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.

Type
Articles
Copyright
Copyright © Cambridge University Press 1994

Access options

Get access to the full version of this content by using one of the access options below.

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. 163177. 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), 5167.CrossRefGoogle Scholar
Fisher, D. (1987). Knowledge acquisition via incremental conceptual clustering. Machine Learning 2, 139172.CrossRefGoogle Scholar
Gennari, J.H., Langley, P., & Fisher, D. (1989). Models of incremental concept formation. Artificial Intelligence 40(1–3), 1161.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. 2154. Springer-Verlag, Berlin.CrossRefGoogle Scholar
Maher, M.L. (1990). Process models for design synthesis. AI Magazine, 11(4), 4958.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. 463502. 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. 163177. Morgan Kaufmann, San Mateo, CA.CrossRefGoogle Scholar
Montanari, U. (1974). Networks of constraints, fundamental properties and applications to picture processing. Information Sci. 7, 95132.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), 630634.Google Scholar
Reich, Y. (1990). Converging to “ideal” design knowledge by learning. In Proc. First Int. Workshop Formal Methods Eng. Design, 330349.Google Scholar
Stepp, R.E. (1987). Machine learning from structured objects. Proc. Fourth Int. Workshop Machine Learning, 353363.CrossRefGoogle Scholar
Thagard, P. (1988). Computational Philosophy of Science. MIT Press, Cambridge, MA.Google 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. 3582. Academic Press, New York.CrossRefGoogle Scholar

Full text views

Full text views reflects PDF downloads, PDFs sent to Google Drive, Dropbox and Kindle and HTML full text views.

Total number of HTML views: 0
Total number of PDF views: 39 *
View data table for this chart

* Views captured on Cambridge Core between September 2016 - 5th March 2021. This data will be updated every 24 hours.

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Learning design concepts using machine learning techniques
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

Learning design concepts using machine learning techniques
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

Learning design concepts using machine learning techniques
Available formats
×
×

Reply to: Submit a response


Your details


Conflicting interests

Do you have any conflicting interests? *