学習による可視性最適化を伴う監視・回避ゲームの戦略合成
Strategy Synthesis for Surveillance-Evasion Games with Learning-Enabled Visibility Optimization
敵対的目標の監視を時相論理制約として課しつつ、CNNで情報利得を近似して短期的可視性を最大化する視点を貪欲に選ぶ手法を提案した論文。
著者: Suda Bharadwaj, Louis Ly, Bo Wu, Richard Tsai, Ufuk Topcu
分類: cs.RO
原文アブストラクト
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.
