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

Abdus Salam Khan and Achim Hoffmann

Artificial Intelligence Laboratory
School of Computer Science and Engineering
The University of New South Wales, Sydney 2052, Australia
{askhan,achim}@cse.unsw.edu.au



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.
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