BAIER, Christel, Clemens DUBSLAFF, Ľuboš KORENČIAK, Antonín KUČERA and Vojtěch ŘEHÁK. Mean-Payoff Optimization in Continuous-Time Markov Chains with Parametric Alarms. ACM Transactions on Modeling and Computer Simulation (TOMACS), ACM, 2019, vol. 29, No 4, p. "28:1"-"28:26". ISSN 1049-3301. doi:10.1145/3310225.
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Basic information
Original name Mean-Payoff Optimization in Continuous-Time Markov Chains with Parametric Alarms
Authors BAIER, Christel (276 Germany), Clemens DUBSLAFF (276 Germany), Ľuboš KORENČIAK (703 Slovakia, guarantor, belonging to the institution), Antonín KUČERA (203 Czech Republic, belonging to the institution) and Vojtěch ŘEHÁK (203 Czech Republic, belonging to the institution).
Edition ACM Transactions on Modeling and Computer Simulation (TOMACS), ACM, 2019, 1049-3301.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
Impact factor Impact factor: 1.380
RIV identification code RIV/00216224:14330/19:00107916
Organization unit Faculty of Informatics
UT WoS 000510187600010
Keywords in English parameter synthesis; continuous-time Markov chains; non-Markovian distributions; Markov decision process; policy iteration; generalized semi-Markov process; Markov regenerative process
Tags formela-journal
Tags International impact, Reviewed
Changed by Changed by: doc. RNDr. Vojtěch Řehák, Ph.D., učo 3721. Changed: 20/4/2020 10:54.
Continuous-time Markov chains with alarms (ACTMCs) allow for alarm events that can be non-exponentially distributed. Within parametric ACTMCs, the parameters of alarm-event distributions are not given explicitly and can be the subject of parameter synthesis. In this line, an algorithm is presented that solves the epsilon-optimal parameter synthesis problem for parametric ACTMCs with long-run average optimization objectives. The approach provided in this article is based on a reduction of the problem to finding long-run average optimal policies in semi-Markov decision processes (semi-MDPs) and sufficient discretization of the parameter (i.e., action) space. Since the set of actions in the discretized semi-MDP can be very large, a straightforward approach based on an explicit action-space construction fails to solve even simple instances of the problem. The presented algorithm uses an enhanced policy iteration on symbolic representations of the action space. Soundness of the algorithm is established for parametric ACTMCs with alarm-event distributions that satisfy four mild assumptions, fulfilled by many kinds of distributions. Exemplifying proofs for the satisfaction of these requirements are provided for Dirac, uniform, exponential, Erlang, and Weibull distributions in particular. An experimental implementation shows that the symbolic technique substantially improves the efficiency of the synthesis algorithm and allows us to solve instances of realistic size.
GA18-11193S, research and development projectName: Algoritmy pro diskrétní systémy a hry s nekonečně mnoha stavy
Investor: Czech Science Foundation, Standard Projects
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