Qualifying the expressivity/efficiency tradeoff: reformation-based diagnosis

Authors:
Helmut Prendinger;Mitsuru Ishizuka
Affiliations:
-;-
Venue:
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Year:
1999

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Abstract

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.