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Proceedings Paper

Probabilistic model-based diagnosis system
Author(s): Jiah-Shing Chen; Sargur N. Srihari
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Paper Abstract

Diagnosis of a malfunctioning physical system is the task of identifying those component parts whose failures are responsible for discrepancies between observed and correct system behavior. The goal of interactive diagnosis is to repeatedly select the best information- gathering action to perform until the device is fixed. We developed a probabilistic diagnosis theory that incorporates probabilistic reasoning into model-based diagnosis. In addition to the structural and functional information normally used in model-based diagnosis, probabilities of component failure are also used to solve the two major subtasks of interactive model-based diagnosis: hypothesis generation and action selection. This paper describes a model-based diagnostic system built according to our probabilistic theory. The major contributions of this paper are the incorporation of probabilistic reasoning into model-based diagnosis and the integration of repair as part of diagnosis. The integration of diagnosis and repair makes it possible to effectively troubleshoot failures in complex systems.

Paper Details

Date Published: 1 March 1992
PDF: 12 pages
Proc. SPIE 1707, Applications of Artificial Intelligence X: Knowledge-Based Systems, (1 March 1992); doi: 10.1117/12.56874
Show Author Affiliations
Jiah-Shing Chen, SUNY/Buffalo (United States)
Sargur N. Srihari, SUNY/Buffalo (United States)

Published in SPIE Proceedings Vol. 1707:
Applications of Artificial Intelligence X: Knowledge-Based Systems
Gautam Biswas, Editor(s)

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