Visual design support in dynamic probabilistic networks for driver modelling

被引：0

作者：

Vogler, A ^{[1
]}

Rammelt, P ^{[1
]}

Herbers, J ^{[1
]}

Neumerkel, D ^{[1
]}

机构：

[1] DaimlerChrysler AG, Res & Technol, Intelligent Syst, D-10559 Berlin, Germany

来源：

ECAI 2000: 14TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2000年 / 54卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Understanding inference in probabilistic networks is an important point in the design phase. Their causal structure and locally defined parameters are intuitive to human experts. The global system induced by the local parameters can lead to results not intended by the human expert. Comprehending the behaviour of dynamic probabilistic networks (DPN) for tuning the model is a time consuming task. Therefore this paper introduces tools supporting the design phase. The application of these tools is shown by means of a DPN for human driver modelling.

引用

页码：591 / 595

页数：5

共 50 条

[1] Visual design support for probabilistic network application
Vogler, A
[J]. ARTIFICIAL INTELLIGENCE AND STATISTICS 99, PROCEEDINGS, 1999, : 309 - 314
[2] Modelling driver acceptance of driver support systems
Rahman, Md Mahmudur
Strawderman, Lesley
Lesch, Mary F.
Horrey, William J.
Babski-Reeves, Kari
Garrison, Teena
[J]. ACCIDENT ANALYSIS AND PREVENTION, 2018, 121 : 134 - 147
[3] Layered dynamic probabilistic networks for spatio-temporal modelling
Bui, Hung H.
Venkatesh, Svetha
West, Geoff
[J]. Intelligent Data Analysis, 1999, 3 (05): : 339 - 361
[4] MODELLING PROBABILISTIC WIRELESS NETWORKS
Cerone, Andrea
Hennessy, Matthew
[J]. LOGICAL METHODS IN COMPUTER SCIENCE, 2013, 9 (03)
[5] Planning in driver models using Probabilistic networks
Rammelt, P
[J]. IEEE ROMAN 2002, PROCEEDINGS, 2002, : 87 - 92
[6] Probabilistic dynamic optimization design for support structure of offshore wind turbines
Lu, Qi-Jin
Yang, He-Zhen
[J]. Zhendong yu Chongji/Journal of Vibration and Shock, 2013, 32 (17): : 46 - 51
[7] Probabilistic assessment of visual fatigue caused by stereoscopy using dynamic Bayesian networks
Yuan, Zhongyun
Zhuo, Kai
Zhang, Qiang
Zhao, Chun
Sang, Shengbo
[J]. ACTA OPHTHALMOLOGICA, 2019, 97 (03) : E435 - E441
[8] Modelling dynamic value streams in support of process design and evaluation
Agyapong-Kodua, K.
Ajaefobi, J. O.
Weston, R. H.
[J]. INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2009, 22 (05) : 411 - 427
[9] Modelling Probabilistic Inference Networks and Classification in Probabilistic Datalog
Martinez-Alvarez, Miguel
Roelleke, Thomas
[J]. SCALABLE UNCERTAINTY MANAGEMENT, SUM 2010, 2010, 6379 : 278 - 291
[10] Modelling of nonlinear dynamic systems using support vector neural networks
Chan, WC
Chan, CW
Cheung, KC
Harris, CJ
[J]. ARTIFICIAL INTELLIGENCE IN REAL-TIME CONTROL 2000, 2001, : 213 - 218

← 1 2 3 4 5 →