対象モデルと故障モデルに基づく知識コンパイラIIの構築と評価

抄録

A new framework for a Knowledge Compiler is proposed from two kinds of design principle. One is constructibility (ease to be constructed) and generality in deep knowledge. The other is a new model for investigating failure-cause. Thus the framework is free from the following problems with conventional model-based approach : (1) Domain model must be prepared in advance, (2) Real failure-cause could not be discovered. The Knowlege Compiler has five kinds of deep knowledge with two deep engines. The first four of them are manipulated on the first deep engine based on propagation mechanism of qualitative values. The first deep engine identifies faulty components from an abnormal symptom and generates abnormal symptoms from some trouble-hypothesis. The last of them, which is called a failure model, is manipulated on the second deep engine based on pattern matching mechanism. It is used for treating failure-mechanism. In the Knowledge Compiler, these two deep engines co-operate for finding out real failure-cause. Note that domain model is not prepared in advance but dynamically constructed from the first four kinds of deep knowledge and that the failure model is not heuristics in conventional expert systems but a generic model for treating failure-mechanism. Finally fault trees generated by the Knowledge Compiler are compared with ones made by human experts. As a result of the comparison, it is shown that the Knowledge Compiler is profitable in actual practice.