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Pacific Rim Knowledge Acquisition Workshop

PKAW 2012: Knowledge Management and Acquisition for Intelligent Systems pp 175–187Cite as

Ripple-Down Rules with Censored Production Rules

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7457))

Abstract

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.

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Author information

Authors and Affiliations

  1. University of New South Wales, Sydney, New South Wales, Australia

    Yang Sok Kim & Paul Compton

  2. University of Tasmania, Sandy Bay, Tasmania, Australia

    Byeong Ho Kang

Authors
  1. Yang Sok Kim
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  2. Paul Compton
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  3. Byeong Ho Kang
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Editor information

Editors and Affiliations

  1. Department of Computing, Macquarie University, 2109, North Ryde, NSW, Australia

    Deborah Richards

  2. School of Computing and Information Systems, University of Tasmania, 7000, Hobart, Tasmania, Australia

    Byeong Ho Kang

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© 2012 Springer-Verlag Berlin Heidelberg

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Kim, Y.S., Compton, P., Kang, B.H. (2012). Ripple-Down Rules with Censored Production Rules. In: Richards, D., Kang, B.H. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2012. Lecture Notes in Computer Science(), vol 7457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32541-0_15

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  • DOI: https://doi.org/10.1007/978-3-642-32541-0_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32540-3

  • Online ISBN: 978-3-642-32541-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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