Proceedings of the 11th International Conference on Quantitative Evaluation of Systems (QEST) |
Published by Springer
We present a novel technique to analyze the bounded reachability probability problem for large Markov chains. The essential idea is to incrementally search for sets of paths that lead to the goal region and to choose the sets in a way that allows us to easily determine the probability mass they represent. To effectively analyze the system dynamics using an SMT solver, we employ a finite-precision abstraction on the Markov chain and a custom quantifier elimination strategy. Through experimental evaluation on PRISM benchmark models we demonstrate the feasibility of the approach on models that are out of reach for previous methods.
Probabilistic Z3 is a solver for the bounded reachability problem that uses the symbolic approximation technique described in the following paper: Markus N. Rabe, Christoph M. Wintersteiger, Hillel Kugler, Boyan Yordanov, and Youssef Hamadi, Symbolic Approximation of the Bounded Reachability Probability in Large Markov Chains, QEST’14, Springer, 2014
