Abstract: 
We study the problem of model checking Intervalvalued Discretetime Markov Chains (IDTMC). IDTMCs are discretetime finite Markov Chains for which the exact transition probabilities are riot known. Instead in IDTMCs, each transition is associated with an interval in which the actual transition probability must lie. We consider two semantic interpretations for the uncertainty in the transition probabilities of an IDTMC. In the first interpretation, we think of an IDTMC as representing a (possibly uncountable) family of (classical) discretetime Markov Chains, where each member of the family is a Markov Chain whose transition probabilities lie within the interval range given in the IDTMC. We call this semantic interpretation Uncertain Markov Chains (UMC). In the second semantics for an IDTMC, which we call Interval Markov Decision Process (IMDP), we view the uncertainty as being resolved through nondeterminism. In other words, each time a state is visited, we adversarially pick a transition distribution that respects the interval constraints, and take a probabilistic step according to the chosen distribution. We introduce a logic omegaPCTL that can express liveness, strong fairness, and omegaregular properties (such properties cannot be expressed in PCTL). We show that the omegaPCTL model checking problem for Uncertain Markov Chain semantics is decidable in PSPACE (same as the best known upper bound for PCTL) and for Interval Markov Decision Process semantics is decidable in coNP (improving the previous known PSPACE bound for PCTL). We also show that the qualitative fragment of the logic can lie solved in coNP for the UMC interpretation, and can be solved in polynomial time for a subclass of UMCs. We also prove lower bounds for these model checking problems. We show that the model checking problem of IDTMCs with LTL formulas can be solved for both UMC and IMDP semantics by reduction to the model checking problem of IDTMC with omegaPcTL formulas.
