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

TL;DR

This paper presents a method combining formal guarantees and machine learning to optimize vantage points for surveillance and evasion in a game setting, ensuring constraints are met while maximizing visibility.

Contribution

It introduces a novel approach that integrates formal methods with neural network-based approximation for efficient surveillance strategy synthesis.

Findings

Effective vantage point selection that maintains surveillance constraints.

Neural network approximation accelerates visibility gain computation.

Method scales to complex environments with formal correctness guarantees.

Abstract

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.