Modeling Techniques and Algorithms for Probabilistic Model-Based Diagnosis and Repair

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Citation: Sampath Srinivas. (1995) Modeling Techniques and Algorithms for Probabilistic Model-Based Diagnosis and Repair. In KSL-95-62, 1995.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Sampath Srinivas
title Modeling Techniques and Algorithms for Probabilistic Model-Based Diagnosis and Repair
number KSL-95-62
institution Stanford University
year 1995
Bibtex more
note STAN-CS-95-1553.
Access Paper
abstract Model-based diagnosis centers on the use of a behavioral model of a system to infer diagnoses of anomalous behavior. For model-based diagnosis techniques to become practical, some serious problems in the modeling of uncertainty and in the tractability of uncertainty management have to be addressed. These questions include: How can we tractably generate diagnoses in large systems? Where do the priorprobabilities of component failure come from when modeling a system?How do we tractably compute low-cost repair strategies? How can we do diagnosis even if only partial descriptions of device operation are available? This dissertation seeks to bring model-based diagnosis closer to being a viable technology by addressing these problems.We develop a set of tractable algorithms and modeling techniques that address each of the problems introduced above. Our approach synthesizes the techniques used in model-based diagnosis and techniques from the field of Bayesian networks.

KSL Technical Report ID: KSL-95-62
Facts about Modeling Techniques and Algorithms for Probabilistic Model-Based Diagnosis and RepairRDF feed
Abstract Model-based diagnosis centers on the use o … Model-based diagnosis centers on the use of a behavioral model of a system to infer diagnoses of anomalous behavior. For model-based diagnosis techniques to become practical, some serious problems in the modeling of uncertainty and in the tractability of uncertainty management have to be addressed. These questions include: How can we tractably generate diagnoses in large systems? Where do the priorprobabilities of component failure come from when modeling a system?How do we tractably compute low-cost repair strategies? How can we do diagnosis even if only partial descriptions of device operation are available? This dissertation seeks to bring model-based diagnosis closer to being a viable technology by addressing these problems.We develop a set of tractable algorithms and modeling techniques that address each of the problems introduced above. Our approach synthesizes the techniques used in model-based diagnosis and techniques from the field of Bayesian networks. iques from the field of Bayesian networks.
Author Sampath Srinivas  +
Bibtype techreport  +
Has author Sampath Srinivas  +
Has identifier KSL-95-62  +
Has publishing details 1995  +
Has title Modeling Techniques and Algorithms for Probabilistic Model-Based Diagnosis and Repair  +
Has where published KSL-95-62  +
Has year 1995  +
Institution Stanford University  +
Ksl tr id KSL-95-62  +
Note STAN-CS-95-1553.
Number KSL-95-62  +
Process note NO  +
Title Modeling Techniques and Algorithms for Probabilistic Model-Based Diagnosis and Repair  +
Year 1995  +
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