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Statements

Subject Item
n2:RIV%2F00216224%3A14330%2F14%3A00075875%21RIV15-MSM-14330___
rdf:type
skos:Concept n11:Vysledek
dcterms:description
We present a general framework for applying machine-learning algorithms to the verification of Markov decision processes (MDPs). The primary goal of these techniques is to improve performance by avoiding an exhaustive exploration of the state space. Our framework focuses on probabilistic reachability, which is a core property for verification, and is illustrated through two distinct instantiations. The first assumes that full knowledge of the MDP is available, and performs a heuristic-driven partial exploration of the model, yielding precise lower and upper bounds on the required probability. The second tackles the case where we may only sample the MDP, and yields probabilistic guarantees, again in terms of both the lower and upper bounds, which provides efficient stopping criteria for the approximation. The latter is the first extension of statistical model checking for unbounded properties in MDPs. We present a general framework for applying machine-learning algorithms to the verification of Markov decision processes (MDPs). The primary goal of these techniques is to improve performance by avoiding an exhaustive exploration of the state space. Our framework focuses on probabilistic reachability, which is a core property for verification, and is illustrated through two distinct instantiations. The first assumes that full knowledge of the MDP is available, and performs a heuristic-driven partial exploration of the model, yielding precise lower and upper bounds on the required probability. The second tackles the case where we may only sample the MDP, and yields probabilistic guarantees, again in terms of both the lower and upper bounds, which provides efficient stopping criteria for the approximation. The latter is the first extension of statistical model checking for unbounded properties in MDPs.
dcterms:title
Verification of Markov Decision Processes using Learning Algorithms Verification of Markov Decision Processes using Learning Algorithms
skos:prefLabel
Verification of Markov Decision Processes using Learning Algorithms Verification of Markov Decision Processes using Learning Algorithms
skos:notation
RIV/00216224:14330/14:00075875!RIV15-MSM-14330___
n3:aktivita
n17:S
n3:aktivity
S
n3:dodaniDat
n8:2015
n3:domaciTvurceVysledku
n18:1762834 n18:3503054
n3:druhVysledku
n21:D
n3:duvernostUdaju
n12:S
n3:entitaPredkladatele
n20:predkladatel
n3:idSjednocenehoVysledku
53238
n3:idVysledku
RIV/00216224:14330/14:00075875
n3:jazykVysledku
n5:eng
n3:klicovaSlova
stochastic systems; verification; machine learning; statistical model checking; reinforcement learning
n3:klicoveSlovo
n4:stochastic%20systems n4:machine%20learning n4:reinforcement%20learning n4:statistical%20model%20checking n4:verification
n3:kontrolniKodProRIV
[DE49D9716FC1]
n3:mistoKonaniAkce
Heidelberg Dordrecht London New York
n3:mistoVydani
Heidelberg Dordrecht London New York
n3:nazevZdroje
Automated Technology for Verification and Analysis - 12th International Symposium, ATVA 2014
n3:obor
n6:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
8
n3:rokUplatneniVysledku
n8:2014
n3:tvurceVysledku
Kwiatkowska, Marta Chmelík, Martin Chatterjee, Krishnendu Forejt, Vojtěch Křetínský, Jan Brázdil, Tomáš Ujma, Mateusz Parker, David
n3:typAkce
n14:WRD
n3:zahajeniAkce
2014-01-01+01:00
s:issn
0302-9743
s:numberOfPages
17
n13:doi
10.1007/978-3-319-11936-6_8
n15:hasPublisher
Springer-Verlag
n10:isbn
9783319119359
n16:organizacniJednotka
14330