A FORMAL MODEL FOR EXPERIENCE-AIDED DIAGNOSIS

This paper presents a novel approach to model-based diagnosis. The app roach addresses the two main problems that have prevented model-based diagnostic techniques from being widely used: computational complexity of abduction and inadequacies of device models. A model for automated diagnosis is defined that combines (1) deduction to rule out hypothes es, (2) abduction to generate hypotheses, and (3) induction to recall past experiences and account for potential errors in the device models . A review of the three forms of inference is provided, as well as a d etailed analysis of the relationship between case-based reasoning and induction. The proposed model for diagnosis is used to characterize di agnostic errors and relate them to different types of errors in the de vice models. Experimental results are then described and used to asser t the practicality and the usefulness of the approach. The model prese nted in this paper yields a practical method for solving hard diagnost ic problems al a reasonable computational cost and provides a theoreti cal basis for overcoming the problem of partially incorrect device mod els.