Interval Probabilistic Timed Graph Transformation Systems

Interval Probabilistic Timed Graph Transformation Systems
Author :
Publisher : Universitätsverlag Potsdam
Total Pages : 64
Release :
ISBN-10 : 9783869565026
ISBN-13 : 3869565020
Rating : 4/5 (26 Downloads)

Book Synopsis Interval Probabilistic Timed Graph Transformation Systems by : Maria Maximova

Download or read book Interval Probabilistic Timed Graph Transformation Systems written by Maria Maximova and published by Universitätsverlag Potsdam. This book was released on 2022-05-19 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: The formal modeling and analysis is of crucial importance for software development processes following the model based approach. We present the formalism of Interval Probabilistic Timed Graph Transformation Systems (IPTGTSs) as a high-level modeling language. This language supports structure dynamics (based on graph transformation), timed behavior (based on clocks, guards, resets, and invariants as in Timed Automata (TA)), and interval probabilistic behavior (based on Discrete Interval Probability Distributions). That is, for the probabilistic behavior, the modeler using IPTGTSs does not need to provide precise probabilities, which are often impossible to obtain, but rather provides a probability range instead from which a precise probability is chosen nondeterministically. In fact, this feature on capturing probabilistic behavior distinguishes IPTGTSs from Probabilistic Timed Graph Transformation Systems (PTGTSs) presented earlier. Following earlier work on Interval Probabilistic Timed Automata (IPTA) and PTGTSs, we also provide an analysis tool chain for IPTGTSs based on inter-formalism transformations. In particular, we provide in our tool AutoGraph a translation of IPTGTSs to IPTA and rely on a mapping of IPTA to Probabilistic Timed Automata (PTA) to allow for the usage of the Prism model checker. The tool Prism can then be used to analyze the resulting PTA w.r.t. probabilistic real-time queries asking for worst-case and best-case probabilities to reach a certain set of target states in a given amount of time.


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