Compositional Abstraction for Stochastic Systems

We propose to exploit three-valued abstraction to stochastic systems in a compositional way. This combines the strengths of an aggressive state-based abstraction technique with compositional modeling. Applying this principle to interactive Markov chains yields abstract models that combine interval Markov chains and modal transition systems in a natural and orthogonal way. We prove the correctness of our technique for parallel and symmetric composition and show that it yields lower bounds for minimal and upper bounds for maximal timed reachability probabilities.