Randomized Sensing in Adversarial Environments
Randomized Sensing in Adversarial Environments
复制标题
对抗环境中的随机感知
DOI：
10.5591/978-1-57735-516-8/ijcai11-356
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
D. Golovin
中科院分区：
文献类型：
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作者：
Andreas Krause;A. Roper;D. Golovin
我们应该如何管理传感器网络以最佳的保护关键安全基础架构？我们应该如何协调搜救直升机以在重大灾难发生后最好地找到幸存者？在这两个应用程序中，我们都希望在不确定的对抗环境中控制传感资源。在本文中，我们引入了RSENSE，这是一种有效的算法，每当检测性能满足次数时，就可以保证近乎最佳的随机感应策略，即对于任何固定的对抗场景，自然减少的回报属性。我们的方法结合了游戏理论的技术和次试验优化。 RSENSE算法适用于目标是管理部署的传感器网络或协调移动传感资源（例如无人机）的设置。我们评估了两个现实世界传感问题的算法。
How should we manage a sensor network to optimally guard security-critical infrastructure? How should we coordinate search and rescue helicopters to best locate survivors after a major disaster? In both applications, we would like to control sensing resources in uncertain, adversarial environments. In this paper, we introduce RSENSE, an efficient algorithm which guarantees near-optimal randomized sensing strategies whenever the detection performance satisfies submodularity, a natural diminishing returns property, for any fixed adversarial scenario. Our approach combines techniques from game theory with submodular optimization. The RSENSE algorithm applies to settings where the goal is to manage a deployed sensor network or to coordinate mobile sensing resources (such as unmanned aerial vehicles). We evaluate our algorithms on two real-world sensing problems.