Abstract
Novel applications of formal modelling such as systems biology have highlighted the need to extend formal analysis techniques to domains with pervasive parametric uncertainty. Consequently, machine learning methods for parameter synthesis and uncertainty quantification are playing an increasingly significant role in quantitative formal modelling. In this paper, we introduce a toolbox for parameter synthesis and model checking in uncertain systems based on Gaussian Process emulation and optimisation. The toolbox implements in a user friendly way the techniques described in a series of recent papers at QEST and other primary venues, and it interfaces easily with widely used modelling languages such as PRISM and Bio-PEPA. We describe in detail the architecture and use of the software, demonstrating its application on a case study.
L. Bortolussi—Work partially supported by EU-FET project QUANTICOL (nr. 600708) and by FRA-UniTS.
D. Milios—Work supported by European Research Council under grant MLCS 306999.
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Notes
- 1.
We assume implicitly that T is sufficiently large so that the truth of \(\varphi \) at time 0 can always be established from \(\mathbf {x}\).
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Bortolussi, L., Milios, D., Sanguinetti, G. (2015). U-Check: Model Checking and Parameter Synthesis Under Uncertainty. In: Campos, J., Haverkort, B. (eds) Quantitative Evaluation of Systems. QEST 2015. Lecture Notes in Computer Science(), vol 9259. Springer, Cham. https://doi.org/10.1007/978-3-319-22264-6_6
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