Stochastic Timed Influence Nets

Author

Yan-guang, Zhu ; Yong-lin, Lei

Author_Institution

Sch. of Inf. Syst. & Manage., NUDT, Changsha, China

Volume

12

fYear

2010

fDate

22-24 Oct. 2010

Abstract

The existing Timed Influence Nets (TIN) framework is assumed that delays on arcs are constant. This constraint may turn out to be unrealistic in many real world situations. The proposed parametric enhancements would overcome the above limitation, and enable a system modeler to specify stochastic delay in a dynamic uncertain situation that the existing TIN fails to capture. The new class of models is named Stochastic Timed Influence Nets (STIN). Both TIN and STIN provide an easy-to-read and compact representation to several time-based probabilistic reasoning paradigms.

Keywords

military systems; neural nets; probability; stochastic processes; STIN; arcs delays; dynamic uncertain situation; military operations; stochastic delay; stochastic timed influence nets; time-based probabilistic reasoning paradigm; Bayesian methods; Belief propagation; Computational modeling; Delay; Information processing; Stochastic processes; Tin; probability Propagation algorithm; stochastic belief sequence; stochastic delay; stochastic timed influence nets; timed influence nets;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Application and System Modeling (ICCASM), 2010 International Conference on

Conference_Location

Taiyuan

Print_ISBN

978-1-4244-7235-2

Electronic_ISBN

978-1-4244-7237-6

Type

conf

DOI

10.1109/ICCASM.2010.5622434

Filename

5622434