Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-24559-6_12
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dc.titlePRTS: An approach for model checking probabilistic real-time hierarchical systems
dc.contributor.authorSun, J.
dc.contributor.authorLiu, Y.
dc.contributor.authorSong, S.
dc.contributor.authorDong, J.S.
dc.contributor.authorLi, X.
dc.date.accessioned2013-07-23T09:26:58Z
dc.date.available2013-07-23T09:26:58Z
dc.date.issued2011
dc.identifier.citationSun, J.,Liu, Y.,Song, S.,Dong, J.S.,Li, X. (2011). PRTS: An approach for model checking probabilistic real-time hierarchical systems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6991 LNCS : 147-162. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-24559-6_12" target="_blank">https://doi.org/10.1007/978-3-642-24559-6_12</a>
dc.identifier.isbn9783642245589
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/43173
dc.description.abstractModel Checking real-life systems is always difficult since such systems usually have quantitative timing factors and work in unreliable environment. The combination of real-time and probability in hierarchical systems presents a unique challenge to system modeling and analysis. In this work, we develop an automated approach for verifying probabilistic, real-time, hierarchical systems. Firstly, a modeling language called PRTS is defined, which combines data structures, real-time and probability. Next, a zone-based method is used to build a finite-state abstraction of PRTS models so that probabilistic model checking could be used to calculate the probability of a system satisfying certain property. We implemented our approach in the PAT model checker and conducted experiments with real-life case studies. © 2011 Springer-Verlag.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-24559-6_12
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.contributor.departmentTEMASEK LABORATORIES
dc.description.doi10.1007/978-3-642-24559-6_12
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume6991 LNCS
dc.description.page147-162
dc.identifier.isiutNOT_IN_WOS
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