On the Convergence of Incremental Knowledge Base Construction

  • Tri M. Cao
  • Eric Martin
  • Paul Compton
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3245)

Abstract

Ripple Down Rules is a practical methodology to build knowledge-based systems, which has proved successful in a wide range of commercial applications. However, little work has been done on its theoretical foundations. In this paper, we formalise the key features of the method. We present the process of building a correct knowledge base as a discovery scenario involving a user, an expert, and a system. The user provides data for classification. The expert helps the system to build its knowledge base incrementally, using the output of the latter in response to the last datum provided by the user. In case the system’s output is not satisfactory, the expert guides the system to improve its future performance while not affecting its ability to properly classify past data. We examine under which conditions the sequence of knowledge bases constructed by the system eventually converges to a knowledge base that faithfully represents the target classification function. Our results are in accordance with the observed behaviour of real-life systems.

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References

  1. 1.
    Ambainis, A., Freivalds, R., Smith, C.: Inductive inference with procrastination: back to definitions. Fundam. Inf. 40(1), 1–16 (1999)MATHMathSciNetGoogle Scholar
  2. 2.
    Beydoun, G., Hoffmann, A.: Incremental acquisition of search knowledge. Journal of Human-Computer Studies 52, 493–530 (2000)CrossRefGoogle Scholar
  3. 3.
    Blum, A., Langley, P.: Selection of relevant features and examples in machine learning. Artificial Intelligence 97(1-2), 245–271 (1997)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Colomb, R.: Representation of propositional expert systems as partial functions. Artificial Intelligence 109(1-2), 187–209 (1999)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Compton, P., Edwards, G.: A philosophical basis for knowledge acquisition. Knowledge Acquisition 2, 241–257 (1990)CrossRefGoogle Scholar
  6. 6.
    Compton, P., Ramadan, Z., Preston, P., Le-Gia, T., Chellen, V., Mullholland, M.: A trade-off between domain knowledge and problem solving method power. In: Gaines, B., Musen, M. (eds.) 11th Banff KAW Proceeding, pp. 1–19 (1998)Google Scholar
  7. 7.
    Dash, M., Liu, H.: Consistency-based search in feature selection. Artificial Intelligence 151(1-2), 155–176 (2003)MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Gold, M.E.: Language identification in the limit. Information and Control 10, 447–474 (1967)MATHCrossRefGoogle Scholar
  9. 9.
    Kang, B., Yoshida, K., Motoda, H., Compton, P.: A help desk system with intelligence interface. Applied Artificial Intelligence 11, 611–631 (1997)CrossRefGoogle Scholar
  10. 10.
    Kearns, M.J.: Efficient noise-tolerant learning from statistical queries. In: Proceedings of the 25th ACM Symposium on the Theory of Computing, pp. 392–401. ACM Press, New York (1993)Google Scholar
  11. 11.
    Kwok, R.: Translation of ripple down rules into logic formalisms. In: Dieng, R., Corby, O. (eds.) EKAW 2000. LNCS (LNAI), vol. 1937, pp. 366–379. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  12. 12.
    Motoda, H., Liu, H.: Data reduction: feature aggregation. In: Handbook of data mining and knowledge discovery, pp. 214–218. Oxford University Press, Inc., Oxford (2002)Google Scholar
  13. 13.
    Preston, P., Edwards, G., Compton, P.: A 2000 rule expert system without a knowledge engineer. In: Gaines, B., Musen, M. (eds.) 8th Banff KAW Proceeding (1994)Google Scholar
  14. 14.
    Richards, D., Compton, P.: Taking up the situated cognition challenge with ripple down rules. Journal of Human-Computer Studies 49, 895–926 (1998)CrossRefGoogle Scholar
  15. 15.
    Scheffer, T.: Algebraic foundation and improved methods of induction of ripple down rules. In: Pacific Rim Workshop on Knowledge Acquisition Proceeding (1996)Google Scholar
  16. 16.
    Shiraz, G., Sammut, C.: Combining knowledge acquisition and machine learning to control dynamic systems. In: Proceedings of the 15th International Joint Conference in Artificial Intelligence (IJCAI 1997), pp. 908–913. Morgan Kaufmann, San Francisco (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Tri M. Cao
    • 1
  • Eric Martin
    • 1
    • 2
  • Paul Compton
    • 1
  1. 1.School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia
  2. 2.National ICT Australia Limited 

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