DocumentCode
1889365
Title
An Improved Algorithm of Structure Learning Applied in Organizational Factors Bayesian Belief Network
Author
Yu Tonglan ; Li, Zhang
Author_Institution
Sch. of Comput. Sci. & Technol., Univ. of South China, Hengyang, China
fYear
2010
fDate
25-26 Dec. 2010
Firstpage
1
Lastpage
4
Abstract
The paper first introduces the concept of organizational factors in the socio-technical system and the basic theory of Bayesian Network and then discusses the algorithm of structure learning of Bayesian network. A new algorithm base on dependency analysis is proposed to effectively reduce the number of detecting condition independence. It uses heuristic cutset searching algorithm and orients all the edges in the network before removing superfluous edges. Experiment results indicate that it outperforms the traditional algorithm. Finally, organizational factors Bayesian network is constructed by using the algorithm which is helpful for the nuclear pant to discover the critical factors influencing human reliability in nuclear power plant.
Keywords
belief networks; human factors; learning (artificial intelligence); nuclear engineering computing; nuclear power stations; organisational aspects; dependency analysis; heuristic cutset searching algorithm; human reliability; nuclear power plant; organizational factors Bayesian belief network; sociotechnical system; structure learning; Algorithm design and analysis; Bayesian methods; Complexity theory; Humans; Image edge detection; Object oriented modeling; Reliability;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Engineering and Computer Science (ICIECS), 2010 2nd International Conference on
Conference_Location
Wuhan
ISSN
2156-7379
Print_ISBN
978-1-4244-7939-9
Electronic_ISBN
2156-7379
Type
conf
DOI
10.1109/ICIECS.2010.5677838
Filename
5677838
Link To Document