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Local patching produces compact knowledge bases

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Book cover A Future for Knowledge Acquisition (EKAW 1994)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 867))

Abstract

Knowledge acquisition (KA) encompasses working with the expert to model the domain and a suitable problem solving method as preconditions for building a knowledge based system (KBS) and secondly working with the expert to populate the knowledge base. Ripple Down Rules (RDR) focuses on the second of these activities and allows an expert to populate a knowledge base (KB) without any knowledge engineering assistance. It is based on the idea that since the knowledge an expert provides is a justification of his or her judgment given in a specific context, this knowledge should only be used in the same context. Although the approach has been used for large single classification systems, it has the potential problem that the local nature of the knowledge may result in much repeated knowledge in the KB and much repeated knowledge acquisition. The study here attempts to quantitate and compare KB size and performance for systems built by experts with various levels of expertise and also inductively. The study also proposes a novel way of conducting such studies in that the different levels of expertise were achieved by using simulated experts. The conclusion from this study is that experts are likely to produce reasonably compact and efficient knowledge bases using the Ripple-Down Rule approach.

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References

  1. Catlett, J.: Megainduction: machine learning on very large databases. Machine Learning: Proceedings of the Eigth International Workshop.

    Google Scholar 

  2. Catlett, J.: Ripple-down-rules as a mediating representation in interactive induction. R. Mizoguchi, H. Motoda, J. Boose, B. Gaines and R. Quinlan (eds.):Proceedings of the Second Japanese Knowlege Acquisition for Knowledge-Based Systems Workshop. Kobe, Japan, (1992) 155–170

    Google Scholar 

  3. Cendrowska, J.: An algorithm for inducing modular rules. International Journal of Man-Machine Studies 27 (4) (1987) 349–370

    Google Scholar 

  4. Chandrasekaran, B.: Generic tasks in knowledge-based reasoning: high level building blocks for expert system design. IEEE Expert 1 (3) (1986) 23–30

    Google Scholar 

  5. Clancey, W.: Viewing knowledge bases as qualitative models. IEEE Expert Summer (1989) 9–23

    Google Scholar 

  6. Compton, P.: Insight and knowledge. J. Boose, W. Clancey, B. Gaines and A. Rappaport (eds.):AAAI Spring Symposium: Cognitive aspects of knowledge acquisition. Stanford University, (1992) 57–63

    Google Scholar 

  7. Compton, P., Edwards, G., Kang, B., Lazarus, L., Malor, R., Menzies, T., Preston,P., Srinivasan,A., Sammut, A.: Ripple down rules: possibilities and limitations. J. Boose and B. Gaines (eds.):6th Bannf AAAI Knowledge Acquisition for Knowledge Based Systems Workshop. Banff, (1991) 6.1–6.18

    Google Scholar 

  8. Compton, P., Edwards, G., Srinivasan,A., Malor, R., Preston,P., Kang, B., Lazarus, L.,: Ripple down rules: turning knowledge acquisition into knowledge maintenance. Artificial Intelligence in Medicine 4 (1992) 47–59

    Google Scholar 

  9. Compton, P., Horn, R., Quinlan, R., Lazarus, L.,: Maintaining an expert system. J. R. Quinlan (eds.): Applications of Expert Systems. London: Addison Wesley (1989) 366–385

    Google Scholar 

  10. Compton, P., Jansen, R.: Knowledge in context: a strategy for expert system maintenance. C. Barter and M. Brooks (eds.): Proc AI 88. Berlin: Springer-Verlag (1990) 292–306

    Google Scholar 

  11. Compton, P., Kang, B., Preston, P., Mulholland, M.: Knowledge acquisition without analysis. N. Aussenac, G. Boy, B. Gaines, M. Linster, J.-G. Ganascia and Y. Kodratoff (eds.): Knowledge Acquisition for Knowledge Based Systems. Lecture Notes in AI (723) Berlin: Springer Verlag (1993) 278–299

    Google Scholar 

  12. Compton, P., Jansen, R.: A philosophical basis for knowledge acquisition. Knowledge Acquisition 2 (1990) 241–257

    Google Scholar 

  13. Edwards, G., Compton, P., Malor, R., Srinivasan, A., Lazarus, L.: PEIRS:a pathologist maintained expert system for the interpretation of chemical pathology reports. Pathology 25 (1993) 27–34

    Google Scholar 

  14. Feigenbaum, E.: The art of artificial intelligence: themes and case studies of knowledge engineering. Proceedings of the Joint International Conference on Artificial Intelligence. (1977) 1014–1029

    Google Scholar 

  15. Gaines, B.: An ounce of knowledge is worth aton of data: quantitative studies of the trade-off between expertise and data based on statistically well-founded empirical induction. Proceedings of the Sixth International Workshop on Machine Learning. San Mateo, California, Morgan Kaufmann (1989) 156–159

    Google Scholar 

  16. Gaines, B.: Induction and visualisation of rules with exceptions. J. Boose and B. Gaines (eds.):6th AAAI Knowledge Acquisition for Knowledge Based Systems Workshop. Bannf, (1991) 7.1–7.17

    Google Scholar 

  17. Gaines, B., Compton, P.: Induction of meta-knowledge about knowledge discovery. IEEE Transactions on Knowledge and Data Engineering 5 (6) (1994) 990–992

    Google Scholar 

  18. Gaines, B., Compton, P.: Induction of ripple down rules. A. Adams and L. Sterling (eds.):AI '92. Proceedings of the 5th Australian Joint Conference on Artificial Intelligence. Hobart, Tasmania, World Scientific, Singapore (1992) 349–354

    Google Scholar 

  19. Grossner, C., Preece, A., Chander, G., Radhakrishnan, T., Suen, C.: Exploring the structure of rule based systems. Proceedings of the American Association of Artificial Intelligence. Washington, MIT Press, Cambridge (1993) 704–709

    Google Scholar 

  20. Horn, K., Compton, P., Lazarus, L., Quinlan, J.: An expert system for the interpretation of thyroid assays in a clinical laboratory. Aust Comput J 17 (1) (1985) 7–11

    Google Scholar 

  21. Kang, B., Compton, P.: Knowledge acquisition in context: the multiple classification problem. Proceedings of the Pacific Rim International Conference on Artificial Intelligence. Seoul, (1992) 847–854

    Google Scholar 

  22. Kivinen, J., Mannila, H., Ukkonen, E.: Learning rules with local exceptions. Proceedings Euro-COLT. (1993)

    Google Scholar 

  23. Mansuri, Y., Compton, P., Sammut, C.: A comparison of a manual knowledge acquisition method and an inductive learning method. J. Boose, J. Debenham, B. Gaines and J. Quinlan (eds.):Australian workshop on knowledge acquisition for knowledge based systems. Pokolbin, (1991) 114–132

    Google Scholar 

  24. Menzies, T.: Maintaining procedural knowledge: ripple down functions. A. Adams and L. Stirling (eds.):AI'92, Proceedings of the 5th Australian joint conference on artificial intelligence. Hobart, World Scientific, Singapore (1992) 335–342

    Google Scholar 

  25. Menzies, T., Compton, P.: Knowledge acquisition for performance systems; or: when can ”tests” replace ”tasks”? J. Boose, B. Gaines and M. Musen (eds.):Proceedings of the 8th AAAI-Sponsored Banff Knowledge Acquisition for Knowledge-Based Systems Workshop. Banff, Canada, (1994) 34.1–34.20

    Google Scholar 

  26. O'Hara, K., Shadbolt, N.: AI models as a variety of psychological explanation. International Joint Conference on Artificial Intelligence. Chambery, (1993) 188–193

    Google Scholar 

  27. Preston, P., Edwards, G., Compton, P.: A 1600 rule expert system without knowledge engineers. J. Leibowitz (eds.):Proceedings of the Second World Congress on Expert Systems. Lisbon, Pergamon (1993) in press

    Google Scholar 

  28. Preston, P., Edwards, G., Compton, P.: A 2000 rule expert system without a knowledge engineer. B. Gaines and M. Musen (eds.):Proceedings of the 8th AAAI-Sponsored Banff Knowledge Acquisition for Knowledge-Based Systems Workshop. Banff, Canada, (1994) 17.1–17.10

    Google Scholar 

  29. Quinlan, J.: Simplifying decision trees. Int. J. Man-Machine Studies 27 (1987) 221–234

    Google Scholar 

  30. Quinlan, J., Compton, P., Horn, K., Lazarus, L.: Inductive knowledge acquisition: A case study. Applications of Expert Systems. London: Addison Wesley (1987) 159–173

    Google Scholar 

  31. Siromoney, A., Siromoney, R.: Local exceptions in inductive logic programming. Proceedings of the 14th Machine Intelligence Workshop (1993) to appear

    Google Scholar 

  32. Wielinga, B., Schreiber, A., Breuker, J.: KADS: a modelling approach to knowledge engineering. Knowledge Acqusition 4 (1) (1992) 5–54

    Google Scholar 

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Authors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, PO Box 1, 2033, Kensington, Australia

    P. Compton, P. Preston, B. Kang & T. Yip

Authors
  1. P. Compton
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  2. P. Preston
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  3. B. Kang
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  4. T. Yip
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Editor information

Luc Steels Guus Schreiber Walter Van de Velde

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© 1994 Springer-Verlag Berlin Heidelberg

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Compton, P., Preston, P., Kang, B., Yip, T. (1994). Local patching produces compact knowledge bases. In: Steels, L., Schreiber, G., Van de Velde, W. (eds) A Future for Knowledge Acquisition. EKAW 1994. Lecture Notes in Computer Science, vol 867. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58487-0_6

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  • DOI: https://doi.org/10.1007/3-540-58487-0_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-58487-2

  • Online ISBN: 978-3-540-49006-7

  • eBook Packages: Springer Book Archive

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