Diagnostics and a qualitative model

Abstract

First generation expert systems were using shallow knowledge based on heuristic information to solve a diagnostic problem. This approach has many disadvantages, which can be avoided by using deep knowledge. Diagnostic reasoning based on deep knowledge is called model-based diagnostics. Recently, the use of qualitative modeling in relation to deep knowledge in expert systems has become increasingly important. The main purpose of our contribution is to present the model-based diagnostic approach at a formal level. The originality of the presented formalization is the concept of the diagnostic space, the characterization of the minimal diagnoses, and the measurement. The formalization serves as the theoretical background to prove our view to the design of qualitative system models and to establish the diagnostic architecture called DISY. The qualitative system model in our diagnostic approach needs not to be specially adopted for use in the diagnostic domain. The only requirement is that it must simulate the system behavior expressed by normal or abnormal functioning of its components. Proposed DISY architecture is not complex and simply takes into an account the previous diagnostic result to obtain a new one from the additional observation-measurement (medical tests or examinations) of the system.