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This paper studies a two-player game with a quantitative surveillancerequirement on an adversarial target moving in a discrete state space and asecondary objective to maximize short-term visibility of the environment. Weimpose the surveillance requirement as a temporal logic constraint.We then usea greedy approach to determine vantage points that optimize a notion ofinformation gain, namely, the number of newly-seen states. By using aconvolutional neural network trained on a class of environments, we canefficiently approximate the information gain at each potential vantagepoint.Subsequent vantage points are chosen such that moving to that locationwill not jeopardize the surveillance requirement, regardless of any futureaction chosen by the target. Our method combines guarantees of correctness fromformal methods with the scalability of machine learning to provide an efficientapproach for surveillance-constrained visibility optimization.

Strategy Synthesis for Surveillance-Evasion Games with Learning-Enabled Visibility Optimization