Abstract
Knowledge acquisition (KA) encompasses working with the expert to model the domain and a suitable problem solving method as preconditions for building a knowledge based system (KBS) and secondly working with the expert to populate the knowledge base. Ripple Down Rules (RDR) focuses on the second of these activities and allows an expert to populate a knowledge base (KB) without any knowledge engineering assistance. It is based on the idea that since the knowledge an expert provides is a justification of his or her judgment given in a specific context, this knowledge should only be used in the same context. Although the approach has been used for large single classification systems, it has the potential problem that the local nature of the knowledge may result in much repeated knowledge in the KB and much repeated knowledge acquisition. The study here attempts to quantitate and compare KB size and performance for systems built by experts with various levels of expertise and also inductively. The study also proposes a novel way of conducting such studies in that the different levels of expertise were achieved by using simulated experts. The conclusion from this study is that experts are likely to produce reasonably compact and efficient knowledge bases using the Ripple-Down Rule approach.
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Compton, P., Preston, P., Kang, B., Yip, T. (1994). Local patching produces compact knowledge bases. In: Steels, L., Schreiber, G., Van de Velde, W. (eds) A Future for Knowledge Acquisition. EKAW 1994. Lecture Notes in Computer Science, vol 867. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58487-0_6
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DOI: https://doi.org/10.1007/3-540-58487-0_6
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