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A Formal Model for Experience–Aided Diagnosis
Author(s) -
Féret Michel P.,
Glasgow Janice I.
Publication year - 1997
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/0824-7935.00038
Subject(s) - computer science , inference , artificial intelligence , machine learning , model based reasoning , recall , basis (linear algebra) , computational model , mathematics , philosophy , linguistics , geometry , knowledge representation and reasoning
This paper presents a novel approach to model–based diagnosis. The approach 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 hypotheses, (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 detailed analysis of the relationship between case–based reasoning and induction. The proposed model for diagnosis is used to characterize diagnostic errors and relate them to different types of errors in the device models. Experimental results are then described and used to assert the practicality and the usefulness of the approach. The model presented in this paper yields a practical method for solving hard diagnostic problems at a reasonable computational cost and provides a theoretical basis for overcoming the problem of partially incorrect device models.