  This paper studies a two-player game with a quantitative surveillance
requirement on an adversarial target moving in a discrete state space and a
secondary objective to maximize short-term visibility of the environment. We
impose the surveillance requirement as a temporal logic constraint.We then use
a greedy approach to determine vantage points that optimize a notion of
information gain, namely, the number of newly-seen states. By using a
convolutional neural network trained on a class of environments, we can
efficiently approximate the information gain at each potential vantage
point.Subsequent vantage points are chosen such that moving to that location
will not jeopardize the surveillance requirement, regardless of any future
action chosen by the target. Our method combines guarantees of correctness from
formal methods with the scalability of machine learning to provide an efficient
approach for surveillance-constrained visibility optimization.
