Stochastic temporal logic abstractions: challenges and opportunities.

*(English)*Zbl 06989596
Jansen, David N. (ed.) et al., Formal modeling and analysis of timed systems. 16th international conference, FORMATS 2018, Beijing, China, September 4–6, 2018. Proceedings. Cham: Springer (ISBN 978-3-030-00150-6/pbk; 978-3-030-00151-3/ebook). Lecture Notes in Computer Science 11022, 3-16 (2018).

Summary: Reasoning about uncertainty is one of the fundamental challenges in the real-world deployment of many cyber-physical system applications. Several models for capturing environment uncertainty have been suggested in the past, and these typically are parametric models with either Markovian assumptions on the time-evolution of the system, or Gaussian assumptions on uncertainty. In this paper, we propose a framework for creating data-driven abstractions of the environment based on stochastic temporal logics. Such logics allow combining the power of temporal logic-based absractions with powerful stochastic modeling techniques. Our framework allows constructing stochastic models using generalized master equations, which can be viewed as a nonparametric model capturing the dynamic evolution of the probabilities of system variables with time. Furthermore, we show how we can automatically infer temporal logic based abstractions from such a model. We give examples of applications for such a framework, and highlight some of the open problems and challenges in this approach.

For the entire collection see [Zbl 1396.68015].

For the entire collection see [Zbl 1396.68015].

##### MSC:

68Qxx | Theory of computing |