Computing the optimal strategy to commit to
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Playing games for security: an efficient exact algorithm for solving Bayesian Stackelberg games
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Computing optimal randomized resource allocations for massive security games
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Learning and Approximating the Optimal Strategy to Commit To
SAGT '09 Proceedings of the 2nd International Symposium on Algorithmic Game Theory
A double oracle algorithm for zero-sum security games on graphs
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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Extensive transportation networks have become the economic backbone of the modern age. Thus, securing these networks against the increasing threat of terrorism is of vital importance. However, protecting critical infrastructure using limited security resources against intelligent adversaries in the presence of the uncertainty and complexities of the real-world is a major challenge. While game-theoretic approaches have been proposed for security domains, traditional methods cannot scale to realistic problem sizes (up to billions of action combinations), even in the absence of uncertainty. My thesis proposes new models and algorithms that have not only advanced the state of the art in game-theory, but have actually been successfully deployed in the real-world. For instance, IRIS has been in use by the Federal Air Marshal Service for scheduling officers on some international flights since October 2009. My thesis contributes to a very new area that uses insights from large-scale optimization for game-theoretic problems. It represents a successful transition from game-theoretic advancements to real-world applications that are already in use, and it has opened exciting new avenues to greatly expand the reach of game theory.