Abstract
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.
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Acknowledgement
This work was in part supported by The Defense Advanced Research Projects Agency and DARPA Young Faculty Award under grant numbers W911NF-17-1-0076 and N66001-17-1-4044, and the US National Science Foundation (NSF) under CAREER Award CPS-1453860. The views, opinions, and/or findings contained in this article are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the Department of Defense.
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Deshmukh, J.V., Kyriakis, P., Bogdan, P. (2018). Stochastic Temporal Logic Abstractions: Challenges and Opportunities. In: Jansen, D., Prabhakar, P. (eds) Formal Modeling and Analysis of Timed Systems. FORMATS 2018. Lecture Notes in Computer Science(), vol 11022. Springer, Cham. https://doi.org/10.1007/978-3-030-00151-3_1
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