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A New Approach for the Incremental Development of Adaptation Functions for CBR

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 1898)

Abstract

This paper introduces a new approach to building complex adaptation functions for case-based reasoning systems.

We present an incremental method which allows a domain expert to refine the existing adaptation function during use of the system. We lend ideas from Ripple-Down Rules, a proven method for the very effective and efficient acquisition of classification knowledge during the use of a knowledge-based system. In our approach the expert is only required to provide explanations of why, for a given problem, a certain adaptation step should be taken. Incrementally a complex adaptation function as a systematic composition of many simple adaptation functions is developed. This approach is effective with respect to both, the development of highly tailored and complex adaptation functions for CBR as well as the provision of an intuitive and feasible approach for the expert.

The approach has been implemented in our CBR system MIKAS, for the design of menus according to dietary requirements.

While our approach showed very good results in MIKAS, it represents also a promising technique for many other CBR applications.

Keywords

  • Adaptation Function
  • Domain Expert
  • Grapefruit Juice
  • Abstract Action
  • Adaptation Step

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.

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References

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Khan, A.S., Hoffmann, A. (2000). A New Approach for the Incremental Development of Adaptation Functions for CBR. In: Blanzieri, E., Portinale, L. (eds) Advances in Case-Based Reasoning. EWCBR 2000. Lecture Notes in Computer Science, vol 1898. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44527-7_23

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  • DOI: https://doi.org/10.1007/3-540-44527-7_23

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67933-2

  • Online ISBN: 978-3-540-44527-2

  • eBook Packages: Springer Book Archive

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