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Controlling the complexity in model-based diagnosis

Controlling the complexity in model-based diagnosis We present IDA — an incrementaldiagnosticalgorithm which computes minimal diagnoses from diagnoses, and not from conflicts. As a consequence of this, and by using different models, one can control the computational complexity. In particular, we show that by using a model of the normal behavior, the worst-case complexity of the algorithm to compute thek+1st minimal diagnosis isO(n 2k ), wheren is the number of components. On the practical side, an experimental evaluation indicates that the algorithm can efficiently diagnose devices consisting of a few thousand components. We propose to use a hierarchy of models: first a structural model to compute all minimal diagnoses, then a normal behavior model to find the additional diagnoses if needed, and only then a fault model for their verification. IDA separates model interpretation from the search for minimal diagnoses in the sense that the model interpreter is replaceable. In particular, we show that in some domains it is advantageous to use the constraint logic programming system CLP(ß) instead of a logic programming system like Prolog. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Mathematics and Artificial Intelligence Springer Journals

Controlling the complexity in model-based diagnosis

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References (45)

Publisher
Springer Journals
Copyright
Copyright
Subject
Computer Science; Artificial Intelligence; Mathematics, general; Computer Science, general; Complex Systems
ISSN
1012-2443
eISSN
1573-7470
DOI
10.1007/BF01530747
Publisher site
See Article on Publisher Site

Abstract

We present IDA — an incrementaldiagnosticalgorithm which computes minimal diagnoses from diagnoses, and not from conflicts. As a consequence of this, and by using different models, one can control the computational complexity. In particular, we show that by using a model of the normal behavior, the worst-case complexity of the algorithm to compute thek+1st minimal diagnosis isO(n 2k ), wheren is the number of components. On the practical side, an experimental evaluation indicates that the algorithm can efficiently diagnose devices consisting of a few thousand components. We propose to use a hierarchy of models: first a structural model to compute all minimal diagnoses, then a normal behavior model to find the additional diagnoses if needed, and only then a fault model for their verification. IDA separates model interpretation from the search for minimal diagnoses in the sense that the model interpreter is replaceable. In particular, we show that in some domains it is advantageous to use the constraint logic programming system CLP(ß) instead of a logic programming system like Prolog.

Journal

Annals of Mathematics and Artificial Intelligence – Springer Journals

Published: Apr 5, 2005

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