<span>Probabilistic model checking is a formal verification method, which is used to guarantee the correctness of the computer systems with stochastic behaviors. Reachability probabilities are the main class of properties that are proposed in probabilistic model checking. Some graph-based pre-computation can determine those states for which the reachability probability is exactly zero or one. Iterative numerical methods are used to compute the reachability probabilities for the remaining states. In this paper, we focus on the graph-based pre-computations and propose a heuristic to improve the performance of these pre-computations. The proposed heuristic approximates the set of states that are computed in the standard pre-computation methods. The experiments show that the proposed heuristic can compute a main part of the expected states, while reduces the running time by several orders of magnitude.</span>
Statistically Model Checking PCTL Specifications on Markov Decision Processes via Reinforcement Learning
