Combining Case-Based and Model-Based Reasoning for the Diagnosis of Complex Devices


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Abstract

A novel approach to integrating case-based reasoning with model-based diagnosis is presented. This approach, called Experience Aided Diagnosis (EAD), uses the model of the device and the results of diagnostic tests to index and match cases representing past diagnostic situations. Retrieved cases are then used to overcome errors created by the application of incorrect device models. The diagnostic methodology is described and applied to two real-world devices. Experimental results demonstrate the effectiveness of both the indexing schema and the matching algorithm. The paper discusses how these results can be generalized to multiple fault situations, to other types of device models, and to other applications in the field of an artificial intelligence.

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