Stochastic Graph Transformation with Regions

Paolo Torrini, Reiko Heckel, Istvan Rath, Gabor Bergmann


Graph transformation can be used to implement stochastic simulation of dynamic systems based on semi-Markov processes, extending the standard approach based on Markov chains. The result is a discrete event system, where states are graphs, and events are rule matches associated to general distributions, rather than just exponential ones. We present an extension of this model, by introducing a hierarchical notion of event location, allowing for stochastic dependence of higher-level events on lower-level ones.

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