Mean-Payoff Optimization in Continuous-Time Markov Chains with
Parametric Alarms

J 2019

Mean-Payoff Optimization in Continuous-Time Markov Chains with Parametric Alarms

BAIER, Christel, Clemens DUBSLAFF, Ľuboš KORENČIAK, Antonín KUČERA, Vojtěch ŘEHÁK et. al.

Základní údaje

Originální název

Mean-Payoff Optimization in Continuous-Time Markov Chains with Parametric Alarms

Autoři

BAIER, Christel (276 Německo), Clemens DUBSLAFF (276 Německo), Ľuboš KORENČIAK (703 Slovensko, garant, domácí), Antonín KUČERA (203 Česká republika, domácí) a Vojtěch ŘEHÁK (203 Česká republika, domácí)

Vydání

ACM Transactions on Modeling and Computer Simulation (TOMACS), ACM, 2019, 1049-3301

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 1.380

Kód RIV

RIV/00216224:14330/19:00107916

Organizační jednotka

Fakulta informatiky

DOI

http://dx.doi.org/10.1145/3310225

UT WoS

000510187600010

Klíčová slova anglicky

parameter synthesis; continuous-time Markov chains; non-Markovian distributions; Markov decision process; policy iteration; generalized semi-Markov process; Markov regenerative process

Štítky

formela-journal

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 20. 4. 2020 10:54, doc. RNDr. Vojtěch Řehák, Ph.D.

Anotace

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.

Návaznosti

GA18-11193S, projekt VaV

Název: Algoritmy pro diskrétní systémy a hry s nekonečně mnoha stavy

Investor: Grantová agentura ČR, Algoritmy pro diskrétní systémy a hry s nekonečně mnoha stavy

Citovat

BAIER, Christel, Clemens DUBSLAFF, Ľuboš KORENČIAK, Antonín KUČERA a Vojtěch ŘEHÁK. Mean-Payoff Optimization in Continuous-Time Markov Chains with Parametric Alarms. ACM Transactions on Modeling and Computer Simulation (TOMACS). ACM, 2019, roč. 29, č. 4, s. "28:1"-"28:26", 26 s. ISSN 1049-3301. Dostupné z: https://dx.doi.org/10.1145/3310225.

@article{1598416,
   author = {Baier, Christel and Dubslaff, Clemens and Korenčiak, Ľuboš and Kučera, Antonín and Řehák, Vojtěch},
   article_number = {4},
   doi = {http://dx.doi.org/10.1145/3310225},
   keywords = {parameter synthesis; continuous-time Markov chains; non-Markovian distributions; Markov decision process; policy iteration; generalized semi-Markov process; Markov regenerative process},
   language = {eng},
   issn = {1049-3301},
   journal = {ACM Transactions on Modeling and Computer Simulation (TOMACS)},
   title = {Mean-Payoff Optimization in Continuous-Time Markov Chains with Parametric Alarms},
   url = {http://dx.doi.org/10.1145/3310225},
   volume = {29},
   year = {2019}
}

TY  - JOUR
ID  - 1598416
AU  - Baier, Christel - Dubslaff, Clemens - Korenčiak, Ľuboš - Kučera, Antonín - Řehák, Vojtěch
PY  - 2019
TI  - Mean-Payoff Optimization in Continuous-Time Markov Chains with Parametric Alarms
JF  - ACM Transactions on Modeling and Computer Simulation (TOMACS)
VL  - 29
IS  - 4
SP  - "28:1"-"28:26"
EP  - "28:1"-"28:26"
PB  - ACM
SN  - 10493301
KW  - parameter synthesis
KW  - continuous-time Markov chains
KW  - non-Markovian distributions
KW  - Markov decision process
KW  - policy iteration
KW  - generalized semi-Markov process
KW  - Markov regenerative process
UR  - http://dx.doi.org/10.1145/3310225
L2  - http://dx.doi.org/10.1145/3310225
N2  - 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.
ER  -

BAIER, Christel, Clemens DUBSLAFF, Ľuboš KORENČIAK, Antonín KUČERA a Vojtěch ŘEHÁK. Mean-Payoff Optimization in Continuous-Time Markov Chains with Parametric Alarms. \textit{ACM Transactions on Modeling and Computer Simulation (TOMACS)}. ACM, 2019, roč.~29, č.~4, s.~''28:1''-''28:26'', 26 s. ISSN~1049-3301. Dostupné z: https://dx.doi.org/10.1145/3310225.

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