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Designing Resource-Bounded Reasoners using Bayesian Networks

Metadata Updated: December 6, 2023

In this work we are concerned with the conceptual design of large-scale diagnostic and health management systems that use Bayesian networks. While they are potentially powerful, improperly designed Bayesian networks can result in too high memory requirements or too long inference times, to they point where they may not be acceptable for real-time diagnosis and health management in resource-bounded systems such as NASA's aerospace vehicles. We investigate the clique tree clustering approach to Bayesian network inference, where increasing the size and connectivity of a Bayesian network typically also increases clique tree size. This paper combines techniques for analytically characterizing clique tree growth with bounds on clique tree size imposed by resource constraints, thereby aiding the design and optimization of large-scale Bayesian networks in resource-bounded systems. We provide both theoretical and experimental results, and illustrate our approach using a NASA case study.


O. J. Mengshoel, “Designing Resource-Bounded Reasoners using Bayesian Networks: System Health Monitoring and Diagnosis”, In Proc. of the 18th International Workshop on Principles of Diagnosis (DX-07), Nashville, TN, May 2007.

BibTex Reference:

@inproceedings{mengshoel07designing, author = "Mengshoel, O. J.", title = "Designing Resource-Bounded Reasoners using {Bayesian} Networks: System Health Monitoring and Diagnosis", booktitle = {Proceedings of the 18th International Workshop on Principles of Diagnosis (DX-07)}, year = {2007}, pages = {330--337}, address = {Nashville, TN}, }

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Metadata Created Date November 12, 2020
Metadata Updated Date December 6, 2023
Data Update Frequency irregular

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date November 12, 2020
Metadata Updated Date December 6, 2023
Publisher Dashlink
Identifier DASHLINK_42
Data First Published 2010-09-10
Data Last Modified 2020-01-29
Public Access Level public
Data Update Frequency irregular
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Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
Harvest Source Title NASA Data.json
Homepage URL
Program Code 026:029
Source Datajson Identifier True
Source Hash 4b954e89447f14253dd0c054e02b1ef7b576e154f69d855cedd4f7b7309dad05
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