Qualifying the Expressivity/Efficiency Tradeoff: Reformation-Based Diagnosis

Helmut Prendinger and Mitsuru Ishizuka, University of Tokyo

This paper presents an approach to model-based diagnosis that first compiles a first-order system description to a propositional representation, and then solves the diagnostic problem as a linear programming instance. Relevance reasoning is employed to isolate parts of the system that are related to certain observation types and to economically instantiate the theory, while methods from operations research offer promising results to generate near-optimal diagnoses efficiently.

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