In this thesis, we discuss the verification of discrete-time Markov decision processes (MDP). First, we present two novel algorithms to efficiently compute mean-payoff queries on MDP. Then, we provide efficient practical implementations of LTL-to-automata translation algorithms, which play a central role in probabilistic LTL model checking. We introduce the novel notion of cores in an MDP, which provide a framework to speed up many approximation-based tasks by automatically removing irrelevant states. Finally, we present a significant step towards risk-aware analysis of probabilistic systems.
«
In this thesis, we discuss the verification of discrete-time Markov decision processes (MDP). First, we present two novel algorithms to efficiently compute mean-payoff queries on MDP. Then, we provide efficient practical implementations of LTL-to-automata translation algorithms, which play a central role in probabilistic LTL model checking. We introduce the novel notion of cores in an MDP, which provide a framework to speed up many approximation-based tasks by automatically removing irrelevant s...
»