Detailed Information on Publication Record
2019
Mean-Payoff Optimization in Continuous-Time Markov Chains with Parametric Alarms
BAIER, Christel, Clemens DUBSLAFF, Ľuboš KORENČIAK, Antonín KUČERA, Vojtěch ŘEHÁK et. al.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
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
United States of America
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
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
Tags
International impact, Reviewed
Změněno: 20/4/2020 10:54, doc. RNDr. Vojtěch Řehák, Ph.D.
Abstract
V originále
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.
Links
| GA18-11193S, research and development project |
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