  Autonomous systems often have logical constraints arising, for example, from
safety, operational, or regulatory requirements. Such constraints can be
expressed using temporal logic specifications. The system state is often
partially observable. Moreover, it could encompass a team of multiple agents
with a common objective but disparate information structures and constraints.
In this paper, we first introduce an optimal control theory for partially
observable Markov decision processes (POMDPs) with finite linear temporal logic
constraints. We provide a structured methodology for synthesizing policies that
maximize a cumulative reward while ensuring that the probability of satisfying
a temporal logic constraint is sufficiently high. Our approach comes with
guarantees on approximate reward optimality and constraint satisfaction. We
then build on this approach to design an optimal control framework for
logically constrained multi-agent settings with information asymmetry. We
illustrate the effectiveness of our approach by implementing it on several case
studies.
