Learning Adversary Behavior in Security Games: A PAC Model Perspective

30 Oct 2015Arunesh SinhaDebarun KarMilind Tambe

Recent applications of Stackelberg Security Games (SSG), from wildlife crime to urban crime, have employed machine learning tools to learn and predict adversary behavior using available data about defender-adversary interactions. Given these recent developments, this paper commits to an approach of directly learning the response function of the adversary... (read more)

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