Diagnostic Reasoning with Multilevel Set-Covering Models

We consider multilevel set-covering models for diagnos- tic reasoning: though a lot of work has been done in this field, knowl- edge acquisition efforts have been investigated only insufficiently. We will show how set-covering models can be build incrementally and how they can be refined by knowledge enhancements or repre- sentational extensions. All these extensions have a primary charac- teristic: they can be applied without changing the basic semantics of the model.

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