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Use of decision-tree induction for process optimization and knowledge refinement of an industrial process

Published online by Cambridge University Press:  27 February 2009

A. Famili
A. Famili
Affiliation:
Knowledge Systems Laboratory, Institute for Information Technology, National Research Council Canada, Ottawa, Ontario, Canada K1A 0R6
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Abstract

Development of expert systems involves knowledge acquisition that can be supported by applying machine learning techniques. The basic idea of using decision-tree induction in process optimization and development of the domain model of electrochemical machining (ECM) is presented. How decision-tree induction is used to build and refine the knowledge base of the process is also discussed.

The idea of developing an intelligent supervisory system with a learning component [Intelligent MAnufacturing FOreman (IMAFO)] that is already implemented is briefly introduced. The results of applying IMAFO for analyzing data from the ECM process are presented. How the domain model of the process (electrochemical machining) is built from the initial known information, and how the results of decision-tree induction can be used to optimize the model of the process and further refine the knowledge base are shown. Two examples are given to demonstrate how new rules (to be included in the knowledge base of an expert system) are generated from the rules induced by IMAFO. The procedure to refine these types of rules is also explained.

Keywords

decision-tree inductionprocess optimization
Type
Research Article
Information
AI EDAM , Volume 8 , Issue 1 , Winter 1994 , pp. 63 - 75
Copyright
Copyright © Cambridge University Press 1994

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References

REFERENCES

Barbehenn, M., & Hutchinson, S. (1991). An integrated architecture for learning and planning in robotic domains. ACM SIGART Bull. 2(4), 29–33.CrossRefGoogle Scholar
Cheng, J. et al. , (1990). Expert system and process optimization techniques for real-time monitoring and control of plasma processes. In Proc. SPIE, Vol. 1392, 373–384.Google Scholar
Craw, S., & Sleeman, D. (1990). Automating the refinement of knowledge based systems. In Proc. Ninth Europ. AI Conf., 167–172.Google Scholar
De Barr, A.E., & Oliver, D.A. (1968). Electrochemical Milling. Mac-Donald, London.Google Scholar
Draper, N.R., & Smith, H. (1966). Applied Regression Analysis. John Wiley & Sons Inc., New York, New York.Google Scholar
Famili, A., & Turney, P. (1991). Intelligently helping human planner in industrial process planning. AIEDAM 5(2), 109–124.CrossRefGoogle Scholar
Faust, C.L. (1971). Fundamentals of Electrochemical Machining. The Electrochemical Society, New Jersey.Google Scholar
Ginsberg, A., Weiss, S.M., & Politakis, P. (1988). Automatic knowledge base refinement for classification systems. Artif. Intell. 35(2), 197–226.CrossRefGoogle Scholar
Holder, L.B. (1990). Application of machine learning to the maintenance of knowledge base performance. In Proc. Third Int. Conf. Industrial and Engineering Applications of AI and Expert Systems, 1005–1012.Google Scholar
Indurkhya, N., & Weiss, S.M. (1991). Iterative rule induction methods. J. Appl. Intel. 1, 43–54.CrossRefGoogle Scholar
Garrett, P., William Lee, C., & LeClair, S.R. (1987). Qualitative process automation vs. quantitative process control. In Proc. Ame. Control Conf, 1368–1373.Google Scholar
LeClair, S.R., & Abrams, F. (1988). Qualitative process automation. In Proc. 27th IEEE Conf Decision and Contr., 558–563.CrossRefGoogle Scholar
Michalski, R.S. (1986). Understanding the nature of learning: Issues and research directions. In Machine Learning: An Artificial Intelligence Approach, Volume II(Carbonell, J.G. et al. , Eds.), pp. 3–25. Morgan Kaufmann, Los Altos, California.Google Scholar
Mitchell, T.M. (1982). Generalization as search. Artif Intell. 18(2), 203–226.CrossRefGoogle Scholar
Mittal, S., & Frayman, F. (1989). Towards a generic model of configuration tasks. In Proc. Eleventh Int. Joint Conf. Artificial Intelligence, pp. 1395–1401. Morgan Kaufmann, Los Altos, California.Google Scholar
Nedelec, C., & Causse, K. (1992). Knowledge refinement using knowledge acquisition and machine learning methods. In Current Developments in Knowledge Acquisition-EKAW ’92, (Wetter, T. et al. , Eds.), Springer-Verlag, Berlin.Google Scholar
Politakis, P., & Weiss, S.M. (1984). Using empirical analysis to refine expert system knowledge-bases. Artif. Intell. 22(1), 23–48.CrossRefGoogle Scholar
Quinlan, J.R. (1983). Learning efficient classification procedures and their application to chess end games. In Machine Learning: An Artificial Intelligence Approach, Volume I (Carbonell, J.G. et al. , Eds.), pp. 463–482. Morgan Kaufmann, Los Altos, California.Google Scholar
Quinlan, J.R. (1986). Induction of decision trees. Machine Learning 1(1), 81–106.CrossRefGoogle Scholar
Quinlan, J.R. (1987a). Simplifying decision trees. Int. J. Man-Machine Studies 27(3), 221–234.CrossRefGoogle Scholar
Quinlan, J.R. (1987b). Generating production rules from decision trees. In Proc. Tenth Int. Joint Conf. Artificial Intelligence, pp. 304–307. Morgan Kaufmann, Los Altos, California.Google Scholar
Quinlan, J.R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann, Los Altos, California.Google Scholar
Risko, D.G. (1989). Electrochemical Machining, SME Technical Paper EE-89–820, Society of Manufacturing Engineers, Dearborn, MI.Google Scholar
Schank, R.C. (1991). Where’s the AI? AI Magazine 12(4), 38–49.Google Scholar
Segre, M.A. (1991). Learning how to plan. Robotics and Autonomous Syst. 8(1–2), 93–111.CrossRefGoogle Scholar
Sriram, D. (1990). Computer-aided Engineering: The Knowledge Frontier (Volume 1: Fundamentals), Course notes distributed by the author, Intelligent Engineering Systems Laboratory, MIT, Cambridge, Massachusetts.Google Scholar
Turney, P., & Famili, A. (1992). Analysis of induced decision trees for industrial process optimization. In Proc. 6th Int. Conf. Systems Research, Informatics and Cybernetics, pp. 19–24.Google Scholar
Widmer, G., Horn, W., & Nagele, B. (1992). Automatic Knowledge Base Refinement: Learning from Examples and Deep Knowledge in Rheumatology, Report No. TR-92–16, Austrian Research Institute for Artificial Intelligence.Google Scholar
Wilkins, D.C. (1988). Knowledge base refinement using apprenticeship learning techniques. In Seventh Nat. Conf. Artificial Intelligence, Vol. I, pp. 646–651. Morgan Kaufmann, Los Altos, California.Google Scholar
Wilkins, D.C. (1990). Knowledge Base Refinement as Improving an Incorrect and Incomplete Domain Theory, Report No. UIUCDS-R-90–1585, University of Illinois, Urbana, Illinois.Google Scholar
Wylie, R.H., & Kamel, M. (1992). Model-based knowledge organization: A framework for constructing high-level control systems. Expert Sys. Applic. 4(3), 285–296.CrossRefGoogle Scholar