Advertisement

Applying Multiple Classification Ripple Round Rules to a Complex Configuration Task

  • Ivan Bindoff
  • Byeong Ho Kang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7106)

Abstract

A new expert systems methodology was developed, building on existing work on the Ripple Down Rules (RDR) method. RDR methods offer a solution to the maintenance problem which has otherwise plagued traditional rule-based expert systems. However, they are, in their classic form, unable to support rules which use existing classifications in their rule conditions. The new method outlined in this paper is suited to multiple classification tasks, and maintains all the significant advantages of previous RDR offerings, while also allowing the creation of rules which use classifications in their conditions. It improves on previous offerings in this field by having fewer restrictions regarding where and how these rules may be used. The method has undergone initial testing on a complex configuration task, which would be practically unsolvable with traditional multiple classification RDR methods, and has performed well, reaching an accuracy in the 90 th percentile after being trained with 1073 rules over the course of classifying 1000 cases, taking ~12 expert hours.

Keywords

ripple down rules multiple classification round configuration knowledge acquisition 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Buchanan, B.: Expert systems: working systems and the research literature. Expert Systems 3, 32–50 (1986)CrossRefGoogle Scholar
  2. 2.
    Bachant, J., McDermott, J.: R1 Revisited: four years in the trenches. American Association for Artificial Intelligence Readings from the AI Magazine, 177–188 (1988)Google Scholar
  3. 3.
    Compton, P., Horn, K., Quinlan, R., Lazarus, L., Ho, K.: Maintaining an expert system. Applications of Expert Systems 2, 366–384 (1989)Google Scholar
  4. 4.
    Suwa, M., Scott, A., Shortliffe, E.: An approach to verifying completeness and consistency in a rule-based expert system. AI Magazine 3, 16–21 (1982)Google Scholar
  5. 5.
    Compton, P., Jansen, R.: A philosophical basis for knowledge acquisition. In: European Knowledge Acquisition for Knowledge-Based Systems, pp. 75–89 (1989)Google Scholar
  6. 6.
    Boose, J.H., Bradshaw, J.M.: Expertise transfer and complex problems: using AQUINAS as a knowledge-acquisition workbench for knowledge-based systems. International Journal of Man-Machine Studies 26, 3–28 (1987)CrossRefGoogle Scholar
  7. 7.
    Compton, P., Kang, B.H., Preston, P., Mulholland, M.: Knowledge Acquisition without Analysis. In: Knowledge Acquisition for Knowledge-Based Systems, pp. 278–299 (1993)Google Scholar
  8. 8.
    Kang, B., Compton, P.: A Maintenance Approach to Case Based Reasoning. In: Haton, J.-P., Keane, M., Manago, M. (eds.) European Workshop on Advances in Case-Based Reasoning, vol. 984, pp. 226–239. Springer, Chantilly (1994)CrossRefGoogle Scholar
  9. 9.
    Preston, P., Edwards, G., Compton, P.: A 2000 Rule Expert System Without a Knowledge Engineer. In: AIII-Sponsored Banff Knowledge Acquisition for Knowledge-Based Systems (1994)Google Scholar
  10. 10.
    Kang, B., Compton, P., Preston, P.: Multiple Classification Ripple Down Rules: Evaluation and Possibilities. In: AIII-Sponsored Banff Knowledge Acquisition for Knowledge-Based Systems (1995)Google Scholar
  11. 11.
    Kang, B.H.: Validating knowledge acquisition: multiple classification ripple-down rules. University of New South Wales, Sydney (1995)Google Scholar
  12. 12.
    Compton, P., Peters, L., Edwards, G., Lavers, T.G.: Experience with ripple-down rules. Knowledge Based Systems 19, 356–362 (2006)CrossRefGoogle Scholar
  13. 13.
    Sarraf, Q., Ellis, G.: Business Rules in Retail: The Tesco.com Story. 2010 (2007)Google Scholar
  14. 14.
    Vazey, M., Richards, D.: Troubleshooting at the Call Centre: A Knowledge-based Approach. In: Hamza, M.H. (ed.) International Conference on Artificial Intelligence and Applications, vol. 23, pp. 721–726. IASTED/ACTA Press, Innsbruck (2005)Google Scholar
  15. 15.
    Bindoff, I., Tenni, P., Peterson, G., Kang, B.H., Jackson, S.: Development of an intelligent decision support system for medication review. Journal of Clinical Pharmacy and Therapeutics 32, 81–88 (2007)CrossRefGoogle Scholar
  16. 16.
    Mulholland, M.: The Evaluation of the Applicability of Artificial Intelligence Software to Solving Problems in Ion Chromatography. University of New South Wales (1995)Google Scholar
  17. 17.
    Beydoun, G., Hoffmann, A.: NRDR for the Acquisition of Search Knowledge. In: Sattar, A. (ed.) Canadian AI 1997. LNCS, vol. 1342, pp. 177–186. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  18. 18.
    Compton, P., Richards, D.: Extending ripple down rules. In: International Conference on Knowledge Engineering and Knowledge Management (1999)Google Scholar
  19. 19.
    Compton, P., Richards, D.: Generalising ripple-down rules. In: Knowledge Engineering and Knowledge Management: Methods, Models, Tools, pp. 2–6 (2000)Google Scholar
  20. 20.
    Kahn, A.B.: Topological sorting of large networks. Communications of the ACM 5, 558–562 (1962)CrossRefzbMATHGoogle Scholar
  21. 21.
    Bindoff, I.K.: Multiple Classification Ripple Round Rules: Classifications as Conditions. University of Tasmania, Hobart (2010)Google Scholar
  22. 22.
    Bindoff, I., Ling, T., Kang, B.H.: Multiple Classification Ripple Round Rules: A Preliminary Study. In: Richards, D., Kang, B.-H. (eds.) PKAW 2008. LNCS, vol. 5465, pp. 76–90. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  23. 23.
    Bindoff, I., Kang, B., Ling, T., Tenni, P., Peterson, G.: Applying MCRDR to a Multidisciplinary Domain. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS (LNAI), vol. 4830, pp. 519–528. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ivan Bindoff
    • 1
  • Byeong Ho Kang
    • 2
  1. 1.School of Computing and Information Systems &, School of PharmacyUniversity of TasmaniaAustralia
  2. 2.School of Computing and Information SystemsUniversity of TasmaniaAustralia

Personalised recommendations