Title :
Stochastic Timed Influence Nets
Author :
Yan-guang, Zhu ; Yong-lin, Lei
Author_Institution :
Sch. of Inf. Syst. & Manage., NUDT, Changsha, China
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;
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
DOI :
10.1109/ICCASM.2010.5622434
