Ripple Down Rules法における知識獲得の特性評価に基づくデフォルト知識の決定規範

概要

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"Ripple Down Rules (RDR)" Method is one of the promising approaches to directly acquire and encode knowledge from human experts. It requires data to be supplied incrementally to the knowledge-base being constructed and new piece of knowledge acquired strongly depends on what is given as the default knowledge, which is used as an implicit outcome when no inference outcome is obtained from the knowledgebase. Therefore, it is important to choose good default knowledge for constructing a knowledge-base which is accurate and compact. Further, many actual data are often noisy and we want the RDR noise resistant. This paper reports experimental Results about the effect of the selection of default knowledge and the amount of noise in data on the performance of RDR using a simulated expert instead of a human expert. The best default knowledge is characterized as the class knowledge that maximizes the minimum description length to encode rules and misclassified cases. We confirmed by extensive experimentation that this criterion is indeed valid and useful in constructing an accurate and compact knowledge-base. We also confirmed that the same criterion holds when the data are noisy.
社団法人人工知能学会の論文
2000-01-01