A framework for model-based repair

Authors:
Ying Sun;Daniel S. Weld
Affiliations:
Department of Computer Science and Engineering, University of Washington, Seattle, WA;Department of Computer Science and Engineering, University of Washington, Seattle, WA
Venue:
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Year:
1993

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Abstract

We describe IRS, a program that combines partial-order planning with GDE-style, model-based diagnosis to achieve an integrated approach to repair. Our system makes three contributions to the field of diagnosis. First, we provide a unified treatment of both information-gathering and state-altering actions via the UWL representation language. Second, we describe a way to use part-replacement operations (in addition to probes) to gather diagnostic information. Finally, we define a cost function for decision making that accounts for both the eventual need to repair broken parts and the dependence of costs on the device state.