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We present a data-driven framework for strategy synthesis for partially-knownswitched stochastic systems. The properties of the system are specified usinglinear temporal logic (LTL) over finite traces (LTLf), which is as expressiveas LTL and enables interpretations over finite behaviors. The framework firstlearns the unknown dynamics via Gaussian process regression. Then, it builds aformal abstraction of the switched system in terms of an uncertain Markovmodel, namely an Interval Markov Decision Process (IMDP), by accounting forboth the stochastic behavior of the system and the uncertainty in the learningstep. Then, we synthesize a strategy on the resulting IMDP that maximizes thesatisfaction probability of the LTLf specification and is robust against allthe uncertainties in the abstraction. This strategy is then refined into aswitching strategy for the original stochastic system. We show that thisstrategy is near-optimal and provide a bound on its distance (error) to theoptimal strategy. We experimentally validate our framework on various casestudies, including both linear and non-linear switched stochastic systems.

Strategy Synthesis for Partially-known Switched Stochastic Systems