The following technical report is available from http://aib.informatik.rwth-aachen.de: Three-Valued Abstraction for Probabilistic Systems Joost-Pieter Katoen, Daniel Klink, Martin Leucker, and Verena Wolf AIB 2007-20 This paper proposes a novel abstraction technique for fully probabilistic systems. The models of our study are classical discrete-time and continuous time Markov chains (DTMCs and CTMCs, for short). A DTMC is a Kripke structure in which each transition is equipped with a discrete probability; in a CTMC, in addition, state residence times are governed by negative exponential distributions. Our abstraction technique fits within the realm of three-valued abstraction methods that have been used successfully for traditional model checking. The key ingredients of our technique are a partitioning of the state space combined with an abstraction of transition probabilities by intervals. The uncertainty of intervals is resolved by history-dependent schedulers that may choose extreme values only. It is shown that this provides a conservative abstraction for both negative and affirmative verification results for a three-valued semantics of PCTL (Probabilistic Computation Tree Logic). In the continuous-time setting, the key idea is to apply abstraction on uniform CTMCs which are readily obtained from general CTMCs. In a similar way as for the discrete case, this is shown to yield a conservative abstraction for a three-valued semantics of CSL (Continuous Stochastic Logic). The verification of abstract DTMCs is inspired by the standard MDP (Markov Decision Process) model-checking problem. Abstract CTMCs can be verified by computing time-bounded reachability probabilities in continuous-time MDPs. Some experiments on an infinite-state stochastic Petri net indicate the feasibility of our abstraction technique.