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Stochastic automata are a formal compositional model for concurrentstochastic timed systems, with general distributions and non-deterministicchoices. Measures of interest are defined over schedulers that resolve thenondeterminism. In this paper we investigate the power of various theoreticallyand practically motivated classes of schedulers, considering the classiccomplete-information view and a restriction to non-prophetic schedulers. Weprove a hierarchy of scheduler classes w.r.t. unbounded probabilisticreachability. We find that, unlike Markovian formalisms, stochastic automatadistinguish most classes even in this basic setting. Verification and strategysynthesis methods thus face a tradeoff between powerful and efficient classes.Using lightweight scheduler sampling, we explore this tradeoff and demonstratethe concept of a useful approximative verification technique for stochasticautomata.

A Hierarchy of Scheduler Classes for Stochastic Automata