Transient Reward Approximation for Continuous-Time Markov Chains


We have a tendency to are interested in the analysis of terribly giant continuous-time Markov chains (CTMCs) with many distinct rates. Such models arise naturally in the context of reliability analysis, e.g., of pc network performability analysis, of power grids, of laptop virus vulnerability, and within the study of crowd dynamics. We use abstraction techniques along with novel algorithms for the computation of bounds on the expected final and accumulated rewards in continuous-time Markov decision processes (CTMDPs). These ingredients are combined in an exceedingly partly symbolic and partly specific (symblicit) analysis approach. In particular, we tend to circumvent the use of multi-terminal call diagrams, as a result of the latter do not work well if facing a massive range of different rates. We tend to demonstrate the sensible applicability and potency of the approach on two case studies.

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