We consider parametric version of fixed-delay continuoustime Markov chains (or equivalently deterministic and stochastic Petri nets, DSPN) where fixed-delay transitions are specified by parameters, rather than concrete values. Our goal is to synthesize values of these parameters that, for a given cost function, minimise expected total cost incurred before reaching a given set of target states. We show that under mild assumptions, optimal values of parameters can be effectively approximated using translation to a Markov decision process (MDP) whose actions correspond to discretized values of these parameters. To this end we identify and overcome several interesting phenomena arising in systems with fixed delays.
CITATION STYLE
Brázdil, T., Korenčiak, L., Krčál, J., Novotný, P., & Řehák, V. (2015). Optimizing performance of continuous-time stochastic systems using timeout synthesis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9259, pp. 141–159). Springer Verlag. https://doi.org/10.1007/978-3-319-22264-6_10
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