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Improving design problem formulations using machine learning

Published online by Cambridge University Press:  27 February 2009

John S. Gero
John S. Gero
Affiliation:
Key Centre of Design Computing, University of Sydney, Sydney, Australia
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Abstract

Most of machine learning in design has focussed on learning generalizations to predict some future behavior of the system under consideration. Such approaches have been applied primarily to the analysis and synthesis stages of designing. There has been little work done relating to the formulation stage. This paper applies a particular machine learning approach to the improvement of the formal description of the design formulation. It applies an evolutionary technique to the problem reformulation to improve the formulation. This results in both a near optimal problem formulation and an improvement in the solution synthesized from that formulation.

Type
Research Abstracts
Information
AI EDAM , Volume 10 , Issue 2: Special Issue: Machine learning in design , April 1996 , pp. 147 - 148
Copyright
Copyright © Cambridge University Press 1996

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References

REFERENCES

Arciszewski, T., Mustafa, Z., & Ziarko, W. (1987). A methodology of design knowledge acquisition for use in learning expert systems. Man-Machine Studies 27, 23–32.CrossRefGoogle Scholar
Carbonell, J.G. (1990). Paradigms for machine learning, In Machine Learning Paradigms and Methods (Carbonell, J., Ed.), pp. 1–10. MIT/Elsevier, Cambridge, MA.Google Scholar
Gero, J.S., Louis, S., & Kundu, S. (1994). Evolutionary learning of novel grammars for design improvement. AIEDAM 8(2), 83–94.CrossRefGoogle Scholar
Goldberg, D. (1989). Genetic algorithms in search, optimization and machine learning. Addision-Wesley, Reading, MA.Google Scholar
Gunaratnam, D., & Gero, J.S. (1993). Neural network learning in structural engineering applications, In Computing in Civil and Building Engineering (Cohen, L.F, Ed.), pp. 1448–1455. ASCE, New York.Google Scholar
Maher, M.L., & Li, H. (1992). Automatically learning preliminary design knowledge from design examples. Microcomputers in Civil Eng. 7, 73–80.CrossRefGoogle Scholar
Maher, M.L., & Li, H. (1993). Adapting conceptual clustering for preliminary structural design. In Computing in Civil and Building Engineering (Cohen, L.F., Ed.), pp. 1432–1439. ASCE, New York.Google Scholar
Mitchell, T., Carbonell, J., & Michalski, R., Eds. (1986). Machine learning: A guide to current research, Kluwer, Boston, MA.CrossRefGoogle Scholar
Rao, R., & Lu, S. (1993). A knowledge-based equation discovery system for engineering domains. IEEE Expert 8(4), 37–42.Google Scholar