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Methods for knowledge acquisition and refinement in second generation expert systems

Authors:

Igor MozeticAuthors Info & Claims

ACM SIGART Bulletin, Issue 108

Pages 63 - 69

https://doi.org/10.1145/63266.63274

Published: 01 April 1989 Publication History

Abstract

First generation expert systems rely on the use of surface knowledge, such as associational or heuristic. Second generation technology is characterized by two additional features: deep knowledge and machine learning. Three second generation methods for knowledge acquisition are reviewed: learning rules from examples, model-based rule learning, and semi-automatic model acquisition. The man-machine process of acquiring and refining knowledge extends the role of expert systems to expert support systems, since both man and machine learn through repeated knowledge refinement cycles. Explanation of solutions and of the knowledge base itself is crucial for this man-machine learning process. An extended expert system shell schema is presented that includes a knowledge acquisition and a knowledge explanation module.

References

[1]

Bohanec, M., Rajkovic, V., &amp; Lavrac, N. (1988) Knowledge explanation in expert systems: A decision support system and machine learning view. Proc. IASTED Intl. Conference on Expert Systems, Geneve, Switzerland.

[2]

Boose, J., &amp; Bradshaw, J.M (1987) AQUINAS: A knowledge acquisition workbench for building knowledge-based systems. Proc. First European Workshop on Knowledge Acquisition for Knowledge-based Systems, Reading University.

[3]

Bratko, I., &amp; Kononenko, I. (1987) Learning diagnostic rules from incomplete and noisy data. In: Phelps, B. (ed.) AI Methods in Statistics, London: Gower Technical Press.

[4]

Bratko, I., Mozetic, I., &amp; Lavrac, N. (1988) Automatic synthesis and compression of cardiological knowledge. In: Hayes, J., Michie, D., &amp; Richards, J. (eds.) Machine Intelligence 11, Oxford: Oxford University Press. Also in: Michie, D., &amp; Bratko, I. (eds.) Expert systems: Automating knowledge acquisition, Addison-Wesley, 1986.

Digital Library

[5]

Bratko, I., Mozetic, I., &amp; Lavrac, N. (1989) KARDIO: A study in deep and qualitative knowledge for expert systems. Boston: The MIT Press (in press).

Digital Library

[6]

Breuker, J., &amp; Wielinga, B. (1987) Knowledge acquisition as modeling expertise: The KADS methodology. Proc. First European Workshop on Knowledge Acquisition for Knowledge-based Systems, Reading University.

[7]

Chambers, R.A., &amp; Michie, D. (1969) Man-machine co-operation on a learning task. In: Parslow, R.D., Prowse, R.W. &amp; Elliot-Green, R. (eds.) Computer Graphics: Techniques and Applications, London: Plenum Press.

[8]

de Kleer, J., &amp; Brown, J.S. (1984) A qualitative physics based on confluences. Artificial Intelligence 24 (1-3), pp. 7-83.

Digital Library

[9]

Feigenbaum, E.A. (1977) The art of artificial intelligence: Themes and case studies of knowledge engineering. Pub. No. STAN-SC-77-621, Stanford, CA: Stanford University, Department of Computer Science.

Digital Library

[10]

Forbus, K.D. (1984) Qualitative process theory. Artificial Intelligence 24 (1-3), pp. 85-168.

Digital Library

[11]

Gaines, B.R. (1987) Knowledge acquisition for expert systems. Proc. First European Workshop on Knowledge Acquisition for Knowledge-based Systems, Reading University.

[12]

Hart, A. (1986) Knowledge acquisition for expert systems. London: Kogan Page.

Digital Library

[13]

Kahn, G., Nowlan, S., &amp; McDermott, J. (1985) MORE: An intelligent knowledge acquisition tool. Proc. Ninth Intl. Joint Conference on Artificial Intelligence, Los Angeles, CA: Morgan Kaufmann.

[14]

Kuipers, B. (1986) Qualitative simulation. Artificial Intelligence 29, pp. 289-338.

Digital Library

[15]

Luconi, F.L., Malone T.W., &amp; Scott Morton, M.S. (1986) Expert Systems: The next challenge for managers. Sloan Management Review.

Digital Library

[16]

Michalski, R.S., Mozetic, I., Hong, J., &amp; Lavrac, N. (1986) The multi-purpose incremental learning system AQ15 and its testing application on three medical domains. Proc. Natl. Conference on Artificial Intelligence, AAAI-86, pp. 1041-1045, Philadelphia: Morgan Kaufmann.

Digital Library

[17]

Michie, D. (1986) Machine learning and knowledge acquisition. In: Michie, D., &amp; Bratko, I. (eds.) Expert systems: Automating knowledge acquisition, Addison-Wesley. Also published as Current developments in expert systems. In: Quinlan, J.R. (ed:) Applications of Expert Systems, Addison-Wesley, 1987.

Digital Library

[18]

Michie, D., Muggleton, S., Riese, C. &amp; Zubrick, S. (1984) RULEMASTER: A second-generation knowledge-acquisition facility. Proc. First Conference on Artificial Intelligence Applications, pp. 591-597, Denver.

[19]

Mozetic, I. (1986) Knowledge extraction through learning from examples. In: Mitchell, T.M., Carbonell, J.G., &amp; Michalski, R.S. (eds.) Machine Learning: A Guide to Current Research, pp. 227-231, Boston: Kluwer Academic Publishers.

Digital Library

[20]

Mozetic, I. (1987a) Learning of qualitative models. In: Bratko, I., &amp; Lavrac, N. (eds.) Progress in machine learning, pp. 201-217, Wilmslow, UK: Sigma Press.

[21]

Mozetic, I. (1987b) The role of abstractions in learning qualitative models. Proc. Fourth Intl. Workshop on Machine Learning, pp. 242-255, Irvine, CA: Morgan Kaufmann.

[22]

Pearce, D.A. (1988) The induction of fault diagnosis systems from qualitative models. Proc. Natl. Conference on Artificial Intelligence, AAAI-88, pp. 353-357, Saint Paul, MN: Morgan Kaufmann.

[23]

Quinlan, J.R. (1986) Induction of decision trees. Machine Learning 1 (1), pp. 81-106.

[24]

Steels, L. (1985) Second generation expert systems. Future Generation Expert Systems 1 (4), pp. 213-221.

Digital Library

[25]

Welbank, M. (1983) A review of knowledge acquisition techniques for expert systems. Ipswich: Martlesham Consultancy Services.

Cited By

Negi A(2022)AI for Social Good—A Faustian BargainComputational Intelligence and Data Analytics10.1007/978-981-19-3391-2_4(63-69)Online publication date: 2-Sep-2022
https://doi.org/10.1007/978-981-19-3391-2_4
Sarkar AQian LPeau AShahriar S(2020)Modeling drivers for successful adoption of green business: an interpretive structural modeling approachEnvironmental Science and Pollution Research10.1007/s11356-020-10490-zOnline publication date: 23-Aug-2020
https://doi.org/10.1007/s11356-020-10490-z
Chithralekha TKuppuswami S(2010)A New Behavior Management Architecture for Language Faculty of an Agent for Task DelegationInternational Journal of Intelligent Information Technologies10.4018/jiit.20100401036:2(44-64)Online publication date: 1-Apr-2010
https://dl.acm.org/doi/10.4018/jiit.2010040103
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Index Terms

Methods for knowledge acquisition and refinement in second generation expert systems
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Learning settings
    2. Machine learning approaches
      1. Rule learning

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Information & Contributors

Information

Published In

cover image ACM SIGART Bulletin

ACM SIGART Bulletin Just Accepted

Special issue on knowledge acquisition

April 1989

205 pages

ISSN:0163-5719

DOI:10.1145/63266

Editor:
Chris Westphal
Univ. of Southern California, Los Angeles

Issue’s Table of Contents

Copyright © 1989 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 April 1989

Published in SIGAI , Issue 108

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Cited By

Negi A(2022)AI for Social Good—A Faustian BargainComputational Intelligence and Data Analytics10.1007/978-981-19-3391-2_4(63-69)Online publication date: 2-Sep-2022
https://doi.org/10.1007/978-981-19-3391-2_4
Sarkar AQian LPeau AShahriar S(2020)Modeling drivers for successful adoption of green business: an interpretive structural modeling approachEnvironmental Science and Pollution Research10.1007/s11356-020-10490-zOnline publication date: 23-Aug-2020
https://doi.org/10.1007/s11356-020-10490-z
Chithralekha TKuppuswami S(2010)A New Behavior Management Architecture for Language Faculty of an Agent for Task DelegationInternational Journal of Intelligent Information Technologies10.4018/jiit.20100401036:2(44-64)Online publication date: 1-Apr-2010
https://dl.acm.org/doi/10.4018/jiit.2010040103
Payne PMendonça EJohnson SStarren J(2007)Conceptual knowledge acquisition in biomedicine: A methodological reviewJournal of Biomedical Informatics10.1016/j.jbi.2007.03.00540:5(582-602)Online publication date: Oct-2007
https://doi.org/10.1016/j.jbi.2007.03.005
Say AKuru S(1996)Qualitative system identification: deriving structure from behaviorArtificial Intelligence10.1016/0004-3702(95)00016-X83:1(75-141)Online publication date: 1-May-1996
https://dl.acm.org/doi/10.1016/0004-3702%2895%2900016-X
LaFrance M(1992)Excavation, Capture, Collection, and Creation: Computer Scientists' Metaphors for Eliciting Human ExpertiseMetaphor and Symbol10.1207/s15327868ms0703&4_37:3(135-156)Online publication date: 1-Sep-1992
https://doi.org/10.1207/s15327868ms0703&4_3
Martinez EBeltramini LLeone HRuiz CHuete E(1992)Knowledge elicitation and structuring for a real-time expert system for monitoring a butadiene extraction systemComputers & Chemical Engineering10.1016/S0098-1354(09)80041-016(S345-S352)Online publication date: May-1992
https://doi.org/10.1016/S0098-1354(09)80041-0
Wusteman J(1992)Explanation-Based Learning: A surveyArtificial Intelligence Review10.1007/BF001557636:3(243-262)Online publication date: 1992
https://doi.org/10.1007/BF00155763
Valtorta M(1991)Knowledge base refinement: A bibliographyApplied Intelligence10.1007/BF001177481:1(87-94)Online publication date: Jul-1991
https://doi.org/10.1007/BF00117748
(1990)BIBLIOGRAPHY OF RECENT MACHINE LEARNING RESEARCH 1985–1989Machine Learning10.1016/B978-0-08-051055-2.50033-X(685-789)Online publication date: 1990
https://doi.org/10.1016/B978-0-08-051055-2.50033-X

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