Reactive Bayesian Network Computation Using Feedback Control: An Empirical Study
Abstract
This paper investigates the challenge of integrating intelligent systems into varying computational platforms and application mixes while providing reactive (or soft real-time) response. We integrate Bayesian network computation with feedback control, thereby achieving our reactive objective. As a case study we investigate fault diagnosis using Bayesian networks. While we consider the likelihood weighting and junction tree propagation Bayesian network inference algorithms in some detail, we hypothesize that the techniques developed can be broadly applied to achieve reactive intelligent systems. In the empirical study of this paper we demonstrate reactive fault diagnosis for an electrical power system.
Cite
Text
Mengshoel et al. "Reactive Bayesian Network Computation Using Feedback Control: An Empirical Study." Conference on Uncertainty in Artificial Intelligence, 2012. doi:10.1184/r1/6710246Markdown
[Mengshoel et al. "Reactive Bayesian Network Computation Using Feedback Control: An Empirical Study." Conference on Uncertainty in Artificial Intelligence, 2012.](https://mlanthology.org/uai/2012/mengshoel2012uai-reactive/) doi:10.1184/r1/6710246BibTeX
@inproceedings{mengshoel2012uai-reactive,
title = {{Reactive Bayesian Network Computation Using Feedback Control: An Empirical Study}},
author = {Mengshoel, Ole J. and Ishihara, Abe and Reed, Erik},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2012},
pages = {44-54},
doi = {10.1184/r1/6710246},
url = {https://mlanthology.org/uai/2012/mengshoel2012uai-reactive/}
}