Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-02T20:39:43.343Z Has data issue: false hasContentIssue false
  • English
  • Français

Learning engineering: The key to automatic knowledge acquisition

Published online by Cambridge University Press:  27 February 2009

Tomasz Arciszewski
Tomasz Arciszewski
Affiliation:
Systems Engineering Department, School of Information Technology and Engineering, George Mason University, Fairfax, VA 22030, U.S.A.
Get access Rights & Permissions [Opens in a new window]

Abstract

Learning engineering is a new subarea of knowledge engineering dealing with the methodological aspects of using learning systems in knowledge acquisition. In this paper, the justification for the development of Learning Engineering is provided, and its major subdomains and research directions are briefly discussed.

Keywords

Learning EngineeringKnowledge AcquisitionLearning SystemAutomated Knowledge Acquisition Methodology
Type
Research Abstracts
Information
AI EDAM , Volume 10 , Issue 2: Special Issue: Machine learning in design , April 1996 , pp. 127 - 129
Copyright
Copyright © Cambridge University Press 1996

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

REFERENCES

Arciszewski, T., Borkowski, A., Dybala, T., Racz, J., & Wojan, P. (1994). Empirical Performance Comparison for Symbolic and Sub-symbolic Learning Systems, Proc. First Int. ASCE Congress 1075–1082.Google Scholar
Arciszewski, T., & Dybala, T. (1992). Evaluation of learning systems: A method and experimental results. Reports of Machine Learning and Inference Laboratory, Center for AI, George Mason University.Google Scholar
Arciszewski, T., Dybala, T., & Wnek, J. (1993). Method for evaluation of learning systems. J. Knowl. Eng. Heuristics 2(4), 22–31.Google Scholar
Arciszewski, T., & Mustafa, M. (1989). Inductive learning process: The user's perspective. In Machine Learning (Forsyth, R., Ed.), pp. 39–61. Chapman and Hall, London.Google Scholar
Bala, J., Bloedorn, E., De Jong, K., Kaufmann, K., Michalski, R.S., Pachoowicz, P., Vafaie, H., Wnek, J., & Zhang, J. (1992). A brief review of AQ learning programs and their applications to the Monks' problems. Reports, Center for Artificial Intelligence, George Mason University.Google Scholar
Bloedorn, E., & Michalski, R.S. (1992). Data-driven constructive induction in AQ17-DCI: A method and experiments. Reports of Machine Learning and Inference Laboratory, Center for AI, George Mason University.Google Scholar
Mustafa, M. (1993). Engineering methodology of automated knowledge acquisition: Structural application. Ph.D. Dissertation, Wayne State University, Detroit, MI.Google Scholar
Mustafa, M. (1995). Preparation of examples for learning systems: A strategy for computer-generated examples. Proc. ASCE Congress on Comput. in Civil Eng., 538–545.Google Scholar
Shavlik, J.W., Mooney, R., & Towell, G.G. (1993). Symbolic and neural learning algorithms. In Readings in Knowledge Acquisition and Learning (Buchanan, B.B. and Wilkins, D.C., Eds.), pp. 445–461. Morgan Kaufmann Publishers, San Mateo, CA.Google Scholar
Szczepanik, W., Arciszewski, T., & Wnek, J. (1995). Empirical performance comparison of two symbolic learning systems based on selective and constructive induction. Proc. Workshop on Machine Learning in Eng., Joint Int. Conf. Artif. Intel., 203–214.Google Scholar
Thrun, S.B., Baja, J., Bloedorn, E., Bratko, I., Cestnik, B., Cheng, J., DeJong, K., Dzerowski, S., Fahlman, S.E., Hamann, R., Kaufman, K., Keller, S., Kononenko, I., Kreuzinger, J., Vafaie, H., Van de Velde, W., Wenzel, W., Wnek, J. & Zhang, J. (1991). The Monk's problem. Report, Carnegie Mellon University.Google Scholar
Weiss, S.M., & Kulikowski, C.A. (1991). Computers that learn. Morgan Kaufmann Publishers, San Mateo, CA.Google Scholar
Wnek, J., & Michalski, R.S. (1992). Experimental comparison of symbolic and subsymbolic learning. J. Knowl. Eng. Heuristics 2(4), 1–21.Google Scholar