A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
Efficient algorithms for online decision problems
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Algorithms for subset selection in linear regression
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Optimal approximation for the submodular welfare problem in the value oracle model
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Computing optimal randomized resource allocations for massive security games
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Near-optimal observation selection using submodular functions
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Efficient informative sensing using multiple robots
Journal of Artificial Intelligence Research
Playing Games with Approximation Algorithms
SIAM Journal on Computing
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IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Game-theoretic resource allocation for malicious packet detection in computer networks
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Protecting moving targets with multiple mobile resources
Journal of Artificial Intelligence Research
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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.