Adaptive Model-Based Diagnosis with Hierarchical Model Scheme

Abstract

This paper describes an adaptive model-based diagnostic mechanism. Although model-based systems are more robust than heuristic-based expert systems, they generally require more computation time. Time consumption can be significantly reduced by using a hierarchical model scheme, which presents views of the device at several different levels of detail. We argue that in order to employ hierarchical models effectively, it is necessary to make economically rational choices concerning the trade-off between the cost of a diagnosis and its precision. The mechanism presented here makes these choices using a model Diagnosability Criterion which estimates how much information could be gained by using a candidate model. It takes into account several important parameters, including the level of diagnosis precision required by the user, the computational resources available, the cost of observations, and the phase of the diagnosis. Experimental results demonstrate the effectiveness of the proposed mechanism.