Abstract
Cyber-physical systems (CPSs) are naturally modelled as reactive systems with nondeterministic and probabilistic dynamics. Model-based verification techniques have proved effective in the deployment of safety-critical CPSs. Central for a successful application of such techniques is the construction of an accurate formal model for the system. Manual construction can be a resource-demanding and error-prone process, thus motivating the design of automata learning algorithms to synthesise a system model from observed system behaviours. This paper revisits and adapts the classic Baum-Welch algorithm for learning Markov decision processes and Markov chains. For the case of MDPs, which typically demand more observations, we present a model-based active learning sampling strategy that choses examples which are most informative w.r.t. the current model hypothesis. We empirically compare our approach with state-of-the-art tools and demonstrate that the proposed active learning procedure can significantly reduce the number of observations required to obtain accurate models.
Originalsprog | Engelsk |
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Titel | Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 |
Redaktører | M. Arif Wani, Ishwar K. Sethi, Weisong Shi, Guangzhi Qu, Daniela Stan Raicu, Ruoming Jin |
Antal sider | 6 |
Forlag | IEEE |
Publikationsdato | 2021 |
Sider | 1203-1208 |
ISBN (Elektronisk) | 9781665443371 |
DOI | |
Status | Udgivet - 2021 |
Begivenhed | 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 - Virtual, Online, USA Varighed: 13 dec. 2021 → 16 dec. 2021 |
Konference
Konference | 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 |
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Land/Område | USA |
By | Virtual, Online |
Periode | 13/12/2021 → 16/12/2021 |
Navn | Proceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 |
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Bibliografisk note
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