About: Verification of Markov Decision Processes using Learning Algorithms

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
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. (en)
Title	Verification of Markov Decision Processes using Learning Algorithms Verification of Markov Decision Processes using Learning Algorithms (en)
skos:prefLabel	Verification of Markov Decision Processes using Learning Algorithms Verification of Markov Decision Processes using Learning Algorithms (en)
skos:notation	RIV/00216224:14330/14:00075875!RIV15-MSM-14330___
http://linked.open...avai/riv/aktivita	S
http://linked.open...avai/riv/aktivity	S
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Brázdil, Tomáš Křetínský, Jan
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Masarykova univerzita / Fakulta informatiky
http://linked.open...dnocenehoVysledku	53238
http://linked.open...ai/riv/idVysledku	RIV/00216224:14330/14:00075875
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	stochastic systems; verification; machine learning; statistical model checking; reinforcement learning (en)
http://linked.open.../riv/klicoveSlovo	stochastic systems verification machine learning reinforcement learning statistical model checking
http://linked.open...ontrolniKodProRIV	[DE49D9716FC1]
http://linked.open...v/mistoKonaniAkce	Heidelberg Dordrecht London New York
http://linked.open...i/riv/mistoVydani	Heidelberg Dordrecht London New York
http://linked.open...i/riv/nazevZdroje	Automated Technology for Verification and Analysis - 12th International Symposium, ATVA 2014
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	8 (xsd:int)
http://linked.open...UplatneniVysledku	2014
http://linked.open...iv/tvurceVysledku	Brázdil, Tomáš Forejt, Vojtěch Kwiatkowska, Marta Parker, David Křetínský, Jan Chatterjee, Krishnendu Chmelík, Martin Ujma, Mateusz
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2014-01-01 (xsd:date)
issn	0302-9743
number of pages	17 (xsd:int)
http://bibframe.org/vocab/doi	10.1007/978-3-319-11936-6_8
http://purl.org/ne...btex#hasPublisher	Springer-Verlag
https://schema.org/isbn	9783319119359
http://localhost/t...ganizacniJednotka	14330

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