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Rapid development of knowledge-based systems via integrated knowledge acquisition

Published online by Cambridge University Press:  12 February 2004

HAO XING ,
SAMUEL H. HUANG  and
J. SHI
HAO XING
Affiliation:
Department of Mechanical, Industrial and Manufacturing Engineering, University of Toledo, Toledo, Ohio 43606, USA
SAMUEL H. HUANG
Affiliation:
Intelligent CAM Systems Laboratory, Department of Mechanical, Industrial and Nuclear Engineering, University of Cincinnati, Cincinnati, Ohio 45221, USA
J. SHI
Affiliation:
Intelligent CAM Systems Laboratory, Department of Mechanical, Industrial and Nuclear Engineering, University of Cincinnati, Cincinnati, Ohio 45221, USA
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Abstract

This paper presents a novel approach, which is based on integrated (automatic/interactive) knowledge acquisition, to rapidly develop knowledge-based systems. Linguistic rules compatible with heuristic expert knowledge are used to construct the knowledge base. A fuzzy inference mechanism is used to query the knowledge base for problem solving. Compared with the traditional interview-based knowledge acquisition, our approach is more flexible and requires a shorter development cycle. The traditional approach requires several rounds of interviews (both structured and unstructured). However, our method involves an optional initial interview, followed by data collection, automatic rule generation, and an optional final interview/rule verification process. The effectiveness of our approach is demonstrated through a benchmark case study and a real-life manufacturing application.

Keywords

Drop Hammer FormingData DiscretizationFuzzy InferenceKnowledge AcquisitionKnowledge-Based SystemRule Simplification
Type
Research Article
Information
AI EDAM , Volume 17 , Issue 3 , August 2003 , pp. 221 - 234
Copyright
© 2003 Cambridge University Press

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References

REFERENCES

Bohanec, M. & Rajkovič, V. (1990). DEX: An expert system shell for decision support. Sistemica 1(1), 145–157.Google Scholar
Catlett, J. (1991). On changing continuous attributes into ordered discrete attributes. In Machine Learning-EWSL-91 (Kodratoff, Y., Ed.), pp. 164–178. Porto, Portugal: LNAI.
Coenen, F. & Bench–Capon, T. (1993). Maintenance of knowledge-based systems: Theory, techniques and tools. New York: Academic Press.
Fisher, R. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics 2, 179–188.CrossRefGoogle Scholar
Gaines, B.R. (1987). An overview of knowledge acquisition and transfer. International Journal of Man–Machine Studies 26, 453–472.Google Scholar
Gonzalez, A.J. & Dankel, D.D. (1993). The Engineering of Knowledge-Based Systems. Englewood Cliffs, NJ: Prentice–Hall.
Ham, I. & Lu, S.C.-Y. (1988). Computer-aided process planning: The present and the future. Annals of the CIRP 37(2), 1–11.Google Scholar
Higa, K. (1996). An approach to improving the maintainability of existing rule bases. Decision Support Systems 18, 23–31.Google Scholar
Huang, S.H., Xing, H., & Wang, G. (2001). Intelligent classification of drop hammer forming process method. International Journal of Advanced Manufacturing Technology 18(2), 89–97.Google Scholar
Kasabov, N.K. (1996). Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems. Fuzzy Sets and Systems 82, 135–149.Google Scholar
Kerber, R. (1992). Discretization of numeric attributes. Proc. Tenth National Conf. Artificial Intelligence, pp. 123–128. Cambridge, MA: MIT Press.
Liu, H. & Setiono, R. (1997). Feature selection via discretization. IEEE Transactions on Knowledge and Data Engineering 9(4), 642–645.Google Scholar
McCluskey, E.J., Jr. (1956). Minimization of Boolean functions. PhD Thesis. Massachusetts Institute of Technology.
Mital, A. & Anand, S. (1994). Handbook of Expert Systems Applications in Manufacturing. New York: Chapman & Hall.
Mitchell, T.M. (1997). Machine Learning. Boston: McGraw–Hill.
Mitta, D.A. (1989). Knowledge acquisition: Human factors issues. Proc. Human Factors Society 33rd Annual Meeting, pp. 351–355.
Motta, E., Eisenstadt, M., Pitman, K., & West, M. (1988). Support for knowledge acquisition in the knowledge engineer's assistant (KEATS). Expert Systems 5(1), 6–28.Google Scholar
Pham, D.T. & Dimov, S.S. (1996). An efficient algorithm for automatic knowledge acquisition. Pattern Recognition 30(7), 1137–1143.Google Scholar
Quinlan, J.R. (1986). Induction of decision trees. Machine Learning 1(1), 81–106.Google Scholar
Quinlan, J.R. (1993). C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann.
Studer, R., Fensel, D., Decker, S., & Benjamins, V.R. (1998). Knowledge engineering: Survey and future directions. In Lecture Notes in Artificial Intelligence 1570, Knowledge-Based Systems: Survey and Future Directions (Puppe, F., Ed.). New York: Springer.
Weiss, S.M. & Kulikowski, C.A. (1991). Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems. San Mateo, CA: Morgan Kaufmann.
Wielinga, B.J., Schreiber, A.T., & Breuker, J.A. (1992). KADS: A modelling approach to knowledge engineering. Knowledge Acquisition 4(1), 127–161.Google Scholar
Xiong, N., Litz, L., & Ressom, H. (2002). Learning premises of fuzzy rules for knowledge acquisition in classification problems. Knowledge and Information Systems 4, 96–111.Google Scholar