Strategy synthesis for partially-known switched stochastic systems

Strategy synthesis for partially-known switched stochastic systems

Jackson, John (University of Colorado)
Laurenti, L. (TU Delft Team Luca Laurenti)
Frew, Eric (University of Colorado)
Lahijanian, Morteza (University of Colorado)

2021

We present a data-driven framework for strategy synthesis for partially-known switched stochastic systems. The properties of the system are specified using linear temporal logic (LTL) over finite traces (LTLf), which is as expressive as LTL and enables interpretations over finite behaviors. The framework first learns the unknown dynamics via Gaussian process regression. Then, it builds a formal abstraction of the switched system in terms of an uncertain Markov model, namely an Interval Markov Decision Process (IMDP), by accounting for both the stochastic behavior of the system and the uncertainty in the learning step. Then, we synthesize a strategy on the resulting IMDP that maximizes the satisfaction probability of the LTLf specification and is robust against all the uncertainties in the abstraction. This strategy is then refined into a switching strategy for the original stochastic system. We show that this strategy is near-optimal and provide a bound on its distance (error) to the optimal strategy. We experimentally validate our framework on various case studies, including both linear and non-linear switched stochastic systems.

formal synthesis
gaussian process regression
safe autonomy
switched stochastic systems
uncertain markov decision processes

http://resolver.tudelft.nl/uuid:13a2f9bf-468f-4a01-810a-048d4bb22750

https://doi.org/10.1145/3447928.3456649

Association for Computing Machinery (ACM)

978-1-4503-8339-4

Proceedings of the 24th International Conference on Hybrid Systems (HSCC 2021): Computation and Control (part of CPS-IoT Week)

24th ACM International Conference on Hybrid Systems Computation and Control, HSCC 2021, held as part of the 14th Cyber Physical Systems and Internet-of-Things Week, CPS-IoT Week 2021, 2021-05-19 → 2021-05-21, Virtual, Online, United States

Institutional Repository

conference paper

© 2021 John Jackson, L. Laurenti, Eric Frew, Morteza Lahijanian