Knowledge acquisition by encoding expert rules versus computer induction from examples: a case study involving soybean pathology

Abstract

In view of growing interest in the development of knowledge-based computer consulting systems for various problem domains, the problems of knowledge acquisition have special significance. Current methods of knowledge acquisition rely entirely on the direct representation of knowledge of experts, which usually is a very time and effort consuming task. The paper presents results from an experiment to compare the above method of knowledge acquisition with a method based on inductive learning from examples. The comparison was done in the context of developing rules for soybean disease diagnosis and has demonstrated an advantage of the inductively derived rules in performing a testing task (which involved diagnosing a few hundred cases of soybean diseases).