Exact quantitative probabilistic model checking through rational search

Model checking of systems formalized using probabilistic models such as discrete time Markov chains (DTMCs) and Markov decision processes (MDPs) can be reduced to computing constrained reachability properties. Linear programming methods to compute reachability probabilities for DTMCs and MDPs do not scale to large models. Thus, model checking tools often employ iterative methods to approximate reachability probabilities. These approximations can be far from the actual probabilities, leading to inaccurate model checking results. In this article, we present a new algorithm and its implementation that improves approximate results obtained by scalable techniques like value iteration to compute exact reachability probabilities.