Robust Bayesian Methods for Stackelberg Security Games (Extended Abstract)
Robust Bayesian Methods for Stackelberg Security Games (Extended Abstract)
复制标题
Stackelberg 安全博弈的鲁棒贝叶斯方法（扩展摘要）
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
J. Marecki
中科院分区：
文献类型：
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作者：
Christopher Kiekintveld;J. Marecki
最近的工作已经应用博弈论模型在洛杉矶国际机场（LAX）和联邦空警服务（FAMS）的现实世界的安全问题。对这些领域的分析以领域专家的投入为基础，旨在获取有关潜在恐怖活动和可能的安全对策的最佳可用情报信息。然而，这些模型受到显着的不确定性，特别是在安全领域的情报对手的能力和偏好是非常难以收集。这种不确定性提出了重大挑战，在这些领域应用博弈论分析。我们的实验结果表明，基于完美信息假设的标准解决方案的方法是非常敏感的回报的不确定性，导致低回报的防御者。我们描述了一个模型的贝叶斯Stackelberg游戏，允许一般分布的不确定性的攻击者的收益。我们进行了实验分析的两个算法近似这些游戏的均衡，并表明，所得的解决方案比标准的方法时，有支付的不确定性，得到更好的结果。
Recent work has applied game-theoretic models to real-world security problems at the Los Angeles International Airport (LAX) and Federal Air Marshals Service (FAMS). The analysis of these domains is based on input from domain experts intended to capture the best available intelligence information about potential terrorist activities and possible security countermeasures. Nevertheless, these models are subject to significant uncertainty—especially in security domains where intelligence about adversary capabilities and preferences is very difficult to gather. This uncertainty presents significant challenges for applying game-theoretic analysis in these domains. Our experimental results show that standard solution methods based on perfect information assumptions are very sensitive to payoff uncertainty, resulting in low payoffs for the defender. We describe a model of Bayesian Stackelberg games that allows for general distributional uncertainty over the attacker’s payoffs. We conduct an experimental analysis of two algorithms for approximating equilibria of these games, and show that the resulting solutions give much better results than the standard approach when there is payoff uncertainty.