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Modelling engineering information with machine learning

Published online by Cambridge University Press:  27 February 2009

Yoram Reich
Yoram Reich
Affiliation:
Department of Solid Mechanics, Materials and Structures, Faculty of Engineering, Tel Aviv University, Tel Aviv 69978, Israel
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Abstract

Since the inception of research on machine learning (ML), these techniques have been associated with the task of automated knowledge generation or knowledge reorganization. This association still prevails, as seen in this issue. When the use of ML programs began to attract researchers in engineering design, different existing tools were used to test their utility and gradually, variations of these tools and methods have sprung up. In many cases, the use of these tools was based on availability and not necessarily applicability. When we began working on ML in design, we attempted to follow a different path (Reich, 1991a; Reich & Fenves, 1992) that led to the design of Bridger (Reich & Fenves, 1995), a system for learning bridge synthesis knowledge. Subsequent experiences and further reflection led us to conclude that the process of using ML in design requires careful and systematic treatment for identifying appropriate ML programs for executing the learning tasks we wish to perform (Reich, 1991b, 1993a). Another observation was that the task of creating or reorganizing knowledge for real design tasks is outside the scope of present ML programs. Establishing the practical importance of ML techniques had to start by addressing engineering problems that could benefit from present ML programs.

Type
Research Abstracts
Information
AI EDAM , Volume 10 , Issue 2: Special Issue: Machine learning in design , April 1996 , pp. 171 - 174
Copyright
Copyright © Cambridge University Press 1996

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References

REFERENCES

Levy, S., Subrahmanian, E., Konda, S.L., Coyne, R.F., Westerberg, A.W., & Reich, Y. (1993). An overview of the n–dim environment. Technical Report EDRC–05–65–93, Engineering Design Research Center, Carnegie Mellon University, Pittsburgh, PA.Google Scholar
Reich, Y., & Fenves, S.J. (1989). Integration of generic learning tasks. Technical Report EDRC 12–28–89, Engineering Design Research Center, Carnegie Mellon University, Pittsburgh, PA.Google Scholar
Reich, Y., & Fenves, S.J. (1992). Inductive learning of synthesis knowledge. Int. J. Expert Sysl. Res. Applications 5(4), 275–297.Google Scholar
Reich, Y., & Fenves, S.J. (1995). A system that learns to design cable-stayed bridges. J. Struct. Eng. ASCE 121(7), 1090–1100.CrossRefGoogle Scholar
Reich, Y., Konda, S., Levy, S.N., Monarch, I., & Subrahmanian, E. (1993). New roles for machine learning in design. Artif. Intell. Eng. 8(3), 165–181.CrossRefGoogle Scholar
Reich, Y., Fenves, S.J., & Subrahmanian, E. (1994). Flexible extraction of practical knowledge from bridge databases. Proc. First Cong. Comput. in Civil Eng. 1014–1021.Google Scholar
Reich, Y., Medina, M., Shieh, T.-Y., & Jacobs, T. (1996). Modeling and debugging engineering decision procedures with machine learning. J. Comput. Civil Engin. 10(2), (in press).CrossRefGoogle Scholar
Reich, Y. (1991a). Design knowledge acquisition: Task analysis and a partial implementation. Knowl. Acquisit. 3(3), 237–254.CrossRefGoogle Scholar
Reich, Y. (1991b). Designing integrated learning systems for engineering design. Proc. Eighth Int. Workshop on Machine Learn. 635–639. Morgan Kaufmann, San Mateo, CA.Google Scholar
Reich, Y. (1993a). The development of Bridger: A methodological study of research on the use of machine learning in design. Artif. Intell. Eng. 8(3), 217–231.CrossRefGoogle Scholar
Reich, Y. (1993b). Towards practical machine learning techniques. Proc. First Congress on Comput. Civil Eng. pp. 885–892. ASCE, New York, NY.Google Scholar
Reich, Y. (1995). Measuring the value of knowledge. Int. J. Human-Comput. Stud. 42(1), 3–30.CrossRefGoogle Scholar
Subrahmanian, E., Konda, S.L., Levy, S.N., Reich, Y., Westerberg, A.W., & Monarch, I.A. (1993). Equations aren’t enough: Informal modeling in design. AI EDAM 7(4), 257–274.Google Scholar