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 90th 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 

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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

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