Computing best-response strategies in infinite games of incomplete information
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Monte Carlo approximation in incomplete information, sequential auction games
Decision Support Systems - Special issue: Decision theory and game theory in agent design
Simulation and Gaming
Mathematical Programming: Series A and B
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
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: industrial track
Leader-follower strategies for robotic patrolling in environments with arbitrary topologies
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Computing optimal randomized resource allocations for massive security games
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Game Theoretical Insights in Strategic Patrolling: Model and Algorithm in Normal-Form
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Adversarial uncertainty in multi-robot patrol
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Security games with incomplete information
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Robust Bayesian methods for Stackelberg security games
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
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Recently there has been significant interest in applications of game-theoretic analysis to analyze security resource allocation decisions. Two examples of deployed systems based on this line of research are the ARMOR system in use at the Los Angeles International Airport [20], and the IRIS system used by the Federal Air Marshals Service [25]. Game analysis always begins by developing a model of the domain, often based on inputs from domain experts or historical data. These models inevitably contain significant uncertainty--especially in security domains where intelligence about adversary capabilities and preferences is very difficult to gather. In this work we focus on developing new models and algorithms that capture this uncertainty using continuous payoff distributions. These models are richer and more powerful than previous approaches that are limited to small finite Bayesian game models. We present the first algorithms for approximating equilibrium solutions in these games, and study these algorithms empirically. Our results show dramatic improvements over existing techniques, even in cases where there is very limited uncertainty about an adversaries' payoffs.