Abstract
Autonomous systems often operate in complex environments which can beextremely difficult to model manually at design time. The set of agents and objects in the environment can be hard to predict, let alone their behavior. We present the idea of introspective environment modeling, in which one algorithmically synthesizes, by introspecting on the system, assumptions on the environment under which the system can guarantee correct operation and which can be efficiently monitored at run time. We formalize the problem, illustrate it with examples, and describe an approach to solving a simplified version of the problem in the context of temporal logic planning. We conclude with an outlook to future work.
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Acknowledgments
I gratefully acknowledge the contributions of students and other collaborators in the work that this article draws from. This work was supported in part by NSF grants 1545126 (VeHICaL) and 1646208, the DARPA Assured Autonomy program, the iCyPhy center, and Berkeley Deep Drive.
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Seshia, S.A. (2019). Introspective Environment Modeling. In: Finkbeiner, B., Mariani, L. (eds) Runtime Verification. RV 2019. Lecture Notes in Computer Science(), vol 11757. Springer, Cham. https://doi.org/10.1007/978-3-030-32079-9_2
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DOI: https://doi.org/10.1007/978-3-030-32079-9_2
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