Modeling delay-varying uncertain situations using stochastic timed influence nets
This paper proposes parametric enhancements in the Timed Influence Nets (TIN) based framework for modeling dynamic situations. The existing TIN framework is assumed that time delays on arcs and nodes are constant. This constraint may turn out to be unrealistic in many real world situations. The enhancements proposed in this paper would overcome the above limitation. The proposed parametric enhancements would aid the mathematical modeling of delay-varying influences. The new class of models is named Stochastic Timed Influence Nets (STIN). Both TIN and STIN provide an alternative easy-to-read and compact representation to several time-based probabilistic reasoning paradigms. The Influence Net (IN) based approach has its origin in the Discrete Event Systems modeling. The changes in the probability of an event at different time instants capture the uncertainty associated with the occurrence of the event over a period of time.