Knowledge Compilation and Refinement in Building a Car Diagnostic System
Kei OHTSUKA, Makoto NISHIDA, Yoshiya IKKATAI
The paper investigates a methodology on knowledge compilation and refinement after its acquisition through a process of car diagnostics on the case of abnormal noise and vibration. The knowledge compilation and refinement involve three basic steps; making knowledge hierarchic, finding useful features of knowledge, and designing a simpler decision tree. The better the useful features of knowledge are found, the better the decision tree is designed. This led to effective use of the domain knowledge obtained in cooperation with experts. In order to facilitate the actual fault diagnosis, the decision tree was designed by solving a combinatorial problem to minimize the number of total branches on each primary subset of classification. As a result of the knowledge compilation and refinement, the number of queries on the fault diagnosis was reduced by 83 to 88 percent and the error rate of diagnosis was 10 percent in comparison with the judgement of experts.