Exact quantitative probabilistic model checking through rational search

Model checking 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. On the other hand, specialized techniques employed in existing state-of-the-art exact quantitative model checkers, don’t scale as well as their iterative counterparts. In this work, we present a new model checking algorithm that improves the approximate results obtained by scalable iterative techniques to compute exact reachability probabilities. Our techniques are implemented as an extension of the PRISM model checker and are evaluated against other exact quantitative model checking engines.