  Emerging applications in autonomy require control techniques that take into
account uncertain environments, communication and sensing constraints, while
satisfying highlevel mission specifications. Motivated by this need, we
consider a class of Markov decision processes (MDPs), along with a transfer
entropy cost function. In this context, we study highlevel mission
specifications as co-safe linear temporal logic (LTL) formulae. We provide a
method to synthesize a policy that minimizes the weighted sum of the transfer
entropy and the probability of failure to satisfy the specification. We derive
a set of coupled non-linear equations that an optimal policy must satisfy. We
then use a modified Arimoto-Blahut algorithm to solve the non-linear equations.
Finally, we demonstrated the proposed method on a navigation and path planning
scenario of a Mars rover.
