Scaling-up Security Games with Boundedly Rational Adversaries: A Cutting-plane Approach

To improve the current real-world deployments of Stackelberg security games (SSGs), it is critical now to efficiently incorporate models of adversary bounded rationality in large-scale SSGs. Unfortunately, previously proposed branch-and-price approaches fail to scale-up given the non-convexity of such models, as we show with a realization called COCOMO. Therefore, we next present a novel cutting-plane algorithm called BLADE to scale-up SSGs with complex adversary models, with three key novelties: (i) an efficient scalable separation oracle to generate deep cuts; (ii) a heuristic that uses gradient to further improve the cuts; (iii) techniques for quality-efficiency tradeoff.

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