Ripple-Down Rules with Censored Production Rules

  • Yang Sok Kim
  • Paul Compton
  • Byeong Ho Kang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7457)


Ripple-Down Rules (RDR) has been successfully used to implement incremental knowledge acquisition systems. Its success largely depends on the organisation of rules, and less attention has been paid to its knowledge representation scheme. Most RDR used standard production rules and exception rules. With sequential processing, RDR acquires exception rules for a particular rule only after the rule wrongly classifies cases. We propose censored production rules (CPR), to be used for acquiring exceptions when a new rule is created using censor conditions. This approach is useful when we have a large number of validation cases at hand. We discuss inference and knowledge acquisition algorithms and related issues. The approach can be combined with machine learning techniques to acquire censor conditions.


Knowledge Acquisition Production Rule Minimum Description Length Inference Process Admission Status 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Richards, D.: Two decades of ripple down rules research. The Knowledge Engineering Review 24(2), 159–184 (2009)CrossRefGoogle Scholar
  2. 2.
    Compton, P., Jansen, R.: A philosophical basis for knowledge acquisition. Knowledge Acquisition 2(3), 241–258 (1990)CrossRefGoogle Scholar
  3. 3.
    Kang, B., Compton, P., Preston, P.: Multiple Classification Ripple Down Rules: Evaluation and Possibilities. In: 9th AAAI Sponsored Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, University of Calgary, Banff (1995)Google Scholar
  4. 4.
    Mulholland, M., Preston, P., Sammut, C., Hibbert, B., Compton, P.: An expert system for ion chromatography developed using machine learning and knowledge in context. In: Proceedings of the 6th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, pp. 258–267. Gordon & Breach Science Publishers, Edinburgh (1993)Google Scholar
  5. 5.
    Forgy, C., McDermott, J.P.: OPS, A Domain-Independent Production System Language. In: 5th International Joint Conference on Artificial Intelligence, pp. 933–939. William Kaufmann, Cambridge (1977)Google Scholar
  6. 6.
    Forgy, C.L.: Rete: A fast algorithm for the many pattern/many object pattern match problem. Artificial Intelligence 19(1), 17–37 (1982)CrossRefGoogle Scholar
  7. 7.
    Bench-Capon, T.J.M.: Knowledge Representation - An Approach to Artificial Intelligence. The APIC Series, vol. 32. Academic Press (1990)Google Scholar
  8. 8.
    Pedersen, K.: Well-structured knowledge bases. AI Expert 4(4), 44–55 (1989)Google Scholar
  9. 9.
    Melle, W.v.: A domain-independent production-rule system for consultation programs. In: Proceedings of the 6th International Joint Conference on Artificial Intelligence, vol. 2, pp. 923–925. Morgan Kaufmann Publishers Inc., Tokyo (1979)Google Scholar
  10. 10.
    McDermott, J.: R1: the formative years. Readings from the AI Magazine, 93–101 (1988)Google Scholar
  11. 11.
    Michalski, R.S., Winston, P.H.: Variable precision logic. Artificial Intelligence 29(2), 121–146 (1986)zbMATHCrossRefGoogle Scholar
  12. 12.
    Haddawy, P.: Implementation of and Experiments with a Variable Precision Logic Inference System. In: AAAI 1986, pp. 238–242 (1986)Google Scholar
  13. 13.
    Prati, R.C., Monard, M.C., de Carvalho, A.C.P.L.F.: A Method for Refining Knowledge Rules Using Exceptions. In: ASAI 2003 Simposio Argentino de Inteligencia Artificial, Buenos Aires, Argentina (2003)Google Scholar
  14. 14.
    Cao, T.M., Compton, P.: A simulation framework for knowledge acquisition evaluation. In: Proceedings of the Twenty-Eighth Australasian Conference on Computer Science, vol. 38, pp. 353–360. Australian Computer Society, Inc., Newcastle (2005)Google Scholar
  15. 15.
    Ignizio, J.P.: Introduction to expert systems: the development and implementation of rule-based expert systems (1991)Google Scholar
  16. 16.
    Liu, B., Hu, M., Hsu, W.: Intuitive representation of decision trees using general rules and exceptions. In: 17th National Conference on Artificial Intelligence, pp. 615–620 (2000)Google Scholar
  17. 17.
    Jain, N.K., Bharadwaj, K.K.: Some learning techniques in hierarchical censored production rules (HCPRs) system. International Journal of Intelligent Systems 13(4), 319–344 (1998)zbMATHCrossRefGoogle Scholar
  18. 18.
    Jain, S., Jain, N.K.: A generalized knowledge representation system for context sensitive reasoning: Generalized HCPRs System. Artificial Intelligence Review 30(1-4), 39–52 (2008)CrossRefGoogle Scholar
  19. 19.
    Bharadwaj, K.K., Jain, N.K.: Hierarchical Censored Production Rules (HCPRs) system. Data & Knowledge Engineering 8(1), 19–34 (1992)CrossRefGoogle Scholar
  20. 20.
    Navarro, D.J.: Analyzing the RULEX model of category learning. Journal of Mathematical Psychology 49(4), 259–275 (2005)MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    Nosofsky, R.M., Palmeri, T.J., McKiley, S.C.: Rule-plus-exception model of classification learning. Psychological Review 101, 53–79 (1994)CrossRefGoogle Scholar
  22. 22.
    Yiyu, Y., Fei-Yue, W., Zeng, D., Jue, W.: Rule+exception strategies for security information analysis. IEEE Intelligent Systems 20(5), 52–57 (2005)CrossRefGoogle Scholar
  23. 23.
    Delgado, M., Ruiz, M.D., Sánchez, D.: Mining Exception Rules. In: Bouchon-Meunier, B., Magdalena, L., Ojeda-Aciego, M., Verdegay, J.-L., Yager, R.R. (eds.) Foundations of Reasoning under Uncertainty. STUDFUZZ, vol. 249, pp. 43–63. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  24. 24.
    Liu, B., Hu, M., Hsu, W.: Multi-level organization and summarization of the discovered rules. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 208–217. ACM, Boston (2000)CrossRefGoogle Scholar
  25. 25.
    Dejean, H.: Learning rules and their exceptions. The Journal of Machine Learning Research 2, 669–693 (2002)zbMATHGoogle Scholar
  26. 26.
    Boicu, C., Tecuci, G., Boicu, M., Marcu, D.: Improving the Representation Space through Exception-Based Learning. In: Sixteenth International Flairs Conference, pp. 336–340. AAAI Press (2003)Google Scholar
  27. 27.
    Gaines, B.R., Compton, P.: Induction of ripple-down rules applied to modeling large databases. Journal of Intelligent Information Systems 5(3), 211–228 (1995)CrossRefGoogle Scholar
  28. 28.
    Wada, T., Horiuchi, T., Motoda, H., Washio, T.: Characterization of Default Knowledge in Ripple Down Rules Method. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 284–295. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  29. 29.
    Wada, T., Horiuchi, T., Motota, H., Washio, T.: Integrating Inductive Learning and Knowledge Acquisition in the Ripple Down Rules Method. In: 6th Pacific Knowledge Acquisition Workshop, Sydney, Australia, pp. 325–340 (2000)Google Scholar
  30. 30.
    Compton, P.: Simulating Expertise. In: PKAW 2000: The 2000 Pacific Rim Knowledge Acquisition Workshop, Sydney, Australia (2000)Google Scholar
  31. 31.
    Li, C., Zhang, Y., Li, X.: OcVFDT: one-class very fast decision tree for one-class classification of data streams. In: Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data, pp. 79–86. ACM, Paris (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yang Sok Kim
    • 1
  • Paul Compton
    • 1
  • Byeong Ho Kang
    • 2
  1. 1.University of New South WalesSydneyAustralia
  2. 2.University of TasmaniaAustralia

Personalised recommendations