  Autonomous agents often operate in scenarios where the state is partially
observed. In addition to maximizing their cumulative reward, agents must
execute complex tasks with rich temporal and logical structures. These tasks
can be expressed using temporal logic languages like finite linear temporal
logic (LTL_f). This paper, for the first time, provides a structured framework
for designing agent policies that maximize the reward while ensuring that the
probability of satisfying the temporal logic specification is sufficiently
high. We reformulate the problem as a constrained partially observable Markov
decision process (POMDP) and provide a novel approach that can leverage
off-the-shelf unconstrained POMDP solvers for solving it. Our approach
guarantees approximate optimality and constraint satisfaction with high
probability. We demonstrate its effectiveness by implementing it on several
models of interest.
