Abstract
In this paper, we extend our ensemble-based component model DEECo with the capability to use machine-learning and optimization heuristics in establishing and reconfiguration of autonomic component ensembles. We show how to capture these concepts on the model level and give an example of how such a model can be beneficially used for modeling access-control related problem in the Industry 4.0 settings. We argue that incorporating machine-learning and optimization heuristics is a key feature for modern smart systems which are to learn over the time and optimize their behavior at runtime to deal with uncertainty in their environment.
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Acknowledgment
This work has been partially supported by Charles University institutional funding SVV 260588, partially supported by the Czech Science Foundation project 20-24814J, and partially supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 810115).
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Töpfer, M., Abdullah, M., Bureš, T., Hnětynka, P., Kruliš, M. (2022). Ensemble-Based Modeling Abstractions for Modern Self-optimizing Systems. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning. ISoLA 2022. Lecture Notes in Computer Science, vol 13703. Springer, Cham. https://doi.org/10.1007/978-3-031-19759-8_20
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