Hostname: page-component-8448b6f56d-xtgtn Total loading time: 0 Render date: 2024-04-19T02:06:37.961Z Has data issue: false hasContentIssue false
  • English
  • Français

A machine learning approach to the automatic synthesis of mechanistic knowledge for engineering decision-making

Published online by Cambridge University Press:  27 February 2009

Stephen C.-Y. Lu  and
Kaihu Chen
Stephen C.-Y. Lu
Affiliation:
Knowledge-Based Engineering Systems Research Laboratory, Department of Mechanical and Industrial Engineering, University of Illinois at Urbana–Champaign, Urbana, IL 61801, U.S.A.
Kaihu Chen
Affiliation:
Knowledge-Based Engineering Systems Research Laboratory, Department of Mechanical and Industrial Engineering, University of Illinois at Urbana–Champaign, Urbana, IL 61801, U.S.A.
Get access Rights & Permissions [Opens in a new window]

Abstract

Inductive learning is proposed as a tool for synthesizing domain knowledge from data generated by a model-based simulator. In order to use an inductive engine to generate decision rules, the pre-classification process becomes more complicated in the presence of multiple competing objectives. Instead of relying on a domain expert to perform this pre-classification task, a clustering algorithm is used to eliminate human biases involved in the selection of a classification function for pre-classification. It is shown that the use of a clustering algorithm for pre-classification not only further automates the process of knowledge by synthesizing, but also improves the quality of the rules generated by the inductive engine.

Type
Research Article
Information
AI EDAM , Volume 1 , Issue 2 , May 1987 , pp. 109 - 118
Copyright
Copyright © Cambridge University Press 1987

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

DeVor, R. E., Kapoor, S. G., Hayashida, R. and Subramani, G. 1986. A methodology for the simultaneous engineering of products and manufacturing process. Proceedings of the 14th NAMRC Conference, pp. 535–542.Google Scholar
Fu, H. J., DeVor, R. E. and Kapoor, S. G. 1984. The optimal design of tooth spacing in face milling via a dynamic force model. Proceedings of the 12th NAMRC Conference, p 291.Google Scholar
Lu, S. C.-Y. 1987 a An intelligent framework for engineering decision making, SAE technical paper, No. 870562.Google Scholar
Lu, S. C-Y. 1987 b. Automatic acquisition of domain knowledge from mechanistic simulations for building engineering expert systems, under review by Journal of Engineering for Industry, ASME Transactions.Google Scholar
Michalski, R. S. and Larson, J., 1978. Selection of the most representative training examples and incremental generation of VL1 hypotheses: the underlying methodology and the description of programs ESEL and AQ11. Report No. 867, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IllinoisGoogle Scholar
Michalski, R. S. 1983. A theory and methodology of inductive learning. In Michalski, R. S., Carbonnell, J. G. and Mitchell, T. M. (eds), Machine Learning, An Artificial Intelligence Approach, Palo Alto, CA: Tioga.Google Scholar
Michalski, R. S., and Larson, J. B. 1983. Incremental generation of VL 1 hypotheses: the underlying methodology and the description of program AQ11. Report UIUCDCS-F-83–905, Department of Computer Science, University of Illinois, Urbana, Illinois.Google Scholar
Stepp, R 1984. Conjunctive conceptual clustering: a method and experimentation, Ph D thesis, Department of Computer Science, University of Illinois. Urbana, 1984.Google Scholar
Stepp, R. 1983. Conceptual clustering, inventing goal-oriented classifications of structured objects. In Michalski, R. S., Carbonell, J. G. and Mitchell, T. M. (eds), Machine Learning, An Artificial Intelligence Approach, Palo Alto, CA: Tioga.Google Scholar