  We consider the synthesis of control policies for probabilistic systems,
modeled by Markov decision processes, operating in partially known environments
with temporal logic specifications. The environment is modeled by a set of
Markov chains. Each Markov chain describes the behavior of the environment in
each mode. The mode of the environment, however, is not known to the system.
Two control objectives are considered: maximizing the expected probability and
maximizing the worst-case probability that the system satisfies a given
specification.
