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An Integrated Knowledge Adaption Framework for Case-Based Reasoning Systems

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Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 5712))

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Abstract

The development of effective knowledge adaption techniques is one of the promising solutions to improve the performance of case-based reasoning (CBR) systems. Case-base maintenance becomes a powerful method to refine knowledge in CBR systems. This paper proposes an integrated knowledge adaption framework for CBR systems which contains a meta database component and a maintenance strategies component. The meta database component can help track changes of interested concepts and therefore enable a CBR system to signal a need for maintenance or to invoke adaption on its own. The maintenance strategies component can perform cross-container maintenance operations in a CBR system. This paper also illustrates how the proposed integrated knowledge adaption framework assists decision makers to build dynamic prediction and decision capabilities.

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Author information

Authors and Affiliations

  1. Faculty of Engineering and Information Technology, University of Technology Sydney, PO Box 123, Broadway, NSW, 2007, Australia

    Ning Lu, Jie Lu & Guangquan Zhang

Authors
  1. Ning Lu
  2. Jie Lu
  3. Guangquan Zhang

Editor information

Editors and Affiliations

  1. University of Chile, Republica 701, 8370439, Santiago, Chile

    Juan D. Velásquez  & Sebastián A. Ríos  & 

  2. University of Brighton, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. University of South Australia, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2009 Springer-Verlag Berlin Heidelberg

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Lu, N., Lu, J., Zhang, G. (2009). An Integrated Knowledge Adaption Framework for Case-Based Reasoning Systems. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2009. Lecture Notes in Computer Science(), vol 5712. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04592-9_47

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