Eecient State Classiication of Finite-state Markov Chains

This paper presents an e cient method for state classi cation of nite state Markov chains using BDD-based symbolic techniques. The method exploits the fundamental properties of a Markov chain and classi es the state space by iteratively applying reachability analysis. We compare our method with the current state-of-the-art technique which requires the computation of the transitive closure of the transition relation of a Markov chain. Experiments in over a dozen synchronous and asynchronous systems demonstrate that our method dramatically reduces the CPU time needed, and solves much larger problems because of reduced memory requirements.