Learning Markov models for stationary system behaviors

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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Abstract

Establishing an accurate model for formal verification of an existing hardware or software system is often a manual process that is both time consuming and resource demanding. In order to ease the model construction phase,
methods have recently been proposed for automatically learning accurate system models from data in the form of observations of the target system. Common for these approaches is that they assume the data to consist of multiple independent observation sequences. However, for certain types of systems, in particular many running embedded systems, one would only have access to a single long observation sequence, and in these situations existing automatic learning methods cannot be applied. In this paper, we adapt algorithms for learning variable order Markov chains from a single observation sequence of a target system, so that stationary system properties can be verified using the learned model. Experiments demonstrate that system properties (formulated as stationary probabilities of LTL formulas) can be reliably identified using the learned model.
Original languageEnglish
Title of host publicationNASA Formal Methods : 4th International Symposium, NFM 2012, Norfolk, VA, USA, April 3-5, 2012. Proceedings
EditorsAlwyn E. Goodloe, Suzette Person
Number of pages15
PublisherSpringer
Publication date2012
Pages216-230
ISBN (Print)978-3-642-28890-6
ISBN (Electronic)978-3-642-28891-3
DOIs
Publication statusPublished - 2012
EventNASA Formal Methods Symposium - Norfolk, United States
Duration: 3 Apr 20115 Apr 2012
Conference number: 4

Conference

ConferenceNASA Formal Methods Symposium
Number4
CountryUnited States
CityNorfolk
Period03/04/201105/04/2012
SeriesLecture Notes in Computer Science
Volume7226
ISSN0302-9743

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Embedded systems
Markov processes
Learning systems
Hardware
Experiments
Formal verification

Cite this

Chen, Y., Mao, H., Jaeger, M., Nielsen, T. D., Larsen, K. G., & Nielsen, B. (2012). Learning Markov models for stationary system behaviors. In A. E. Goodloe, & S. Person (Eds.), NASA Formal Methods: 4th International Symposium, NFM 2012, Norfolk, VA, USA, April 3-5, 2012. Proceedings (pp. 216-230). Springer. Lecture Notes in Computer Science, Vol.. 7226 https://doi.org/10.1007/978-3-642-28891-3_22
Chen, Yingke ; Mao, Hua ; Jaeger, Manfred ; Nielsen, Thomas Dyhre ; Larsen, Kim Guldstrand ; Nielsen, Brian. / Learning Markov models for stationary system behaviors. NASA Formal Methods: 4th International Symposium, NFM 2012, Norfolk, VA, USA, April 3-5, 2012. Proceedings. editor / Alwyn E. Goodloe ; Suzette Person. Springer, 2012. pp. 216-230 (Lecture Notes in Computer Science, Vol. 7226).
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Chen, Y, Mao, H, Jaeger, M, Nielsen, TD, Larsen, KG & Nielsen, B 2012, Learning Markov models for stationary system behaviors. in AE Goodloe & S Person (eds), NASA Formal Methods: 4th International Symposium, NFM 2012, Norfolk, VA, USA, April 3-5, 2012. Proceedings. Springer, Lecture Notes in Computer Science, vol. 7226, pp. 216-230, NASA Formal Methods Symposium, Norfolk, United States, 03/04/2011. https://doi.org/10.1007/978-3-642-28891-3_22

Learning Markov models for stationary system behaviors. / Chen, Yingke; Mao, Hua; Jaeger, Manfred; Nielsen, Thomas Dyhre; Larsen, Kim Guldstrand; Nielsen, Brian.

NASA Formal Methods: 4th International Symposium, NFM 2012, Norfolk, VA, USA, April 3-5, 2012. Proceedings. ed. / Alwyn E. Goodloe; Suzette Person. Springer, 2012. p. 216-230.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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Chen Y, Mao H, Jaeger M, Nielsen TD, Larsen KG, Nielsen B. Learning Markov models for stationary system behaviors. In Goodloe AE, Person S, editors, NASA Formal Methods: 4th International Symposium, NFM 2012, Norfolk, VA, USA, April 3-5, 2012. Proceedings. Springer. 2012. p. 216-230. (Lecture Notes in Computer Science, Vol. 7226). https://doi.org/10.1007/978-3-642-28891-3_22