Abstract
A continuous-time Markov decision process (CTMDP) is a generalization of a continuous-time Markov chain in which both probabilistic and nondeterministic choices co-exist. This paper presents an efficient algorithm to compute the maximum (or minimum) probability to reach a set of goal states within a given time bound in a uniform CTMDP, i.e., a CTMDP in which the delay time distribution per state visit is the same for all states. It furthermore proves that these probabilities coincide for (time-abstract) history-dependent and Markovian schedulers that resolve nondeterminism either deterministically or in a randomized way.
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