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
Effective solutions for real-world Stackelberg games: when agents must deal with human uncertainties
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
Quality-bounded solutions for finite Bayesian Stackelberg games: scaling up
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
GUARDS and PROTECT: next generation applications of security games
ACM SIGecom Exchanges
Quality-bounded solutions for finite Bayesian Stackelberg games: scaling up
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Journal of Artificial Intelligence Research
PROTECT: a deployed game theoretic system to protect the ports of the United States
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Playing repeated Stackelberg games with unknown opponents
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
A unified method for handling discrete and continuous uncertainty in Bayesian Stackelberg games
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Game theory for security: an important challenge for multiagent systems
EUMAS'11 Proceedings of the 9th European conference on Multi-Agent Systems
Security games with interval uncertainty
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Security games with contagion: handling asymmetric information
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
An ambiguity aversion framework of security games under ambiguities
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Game theory is fast becoming a vital tool for reasoning about complex real-world security problems, including critical infrastructure protection. The game models for these applications are constructed using expert analysis and historical data to estimate the values of key parameters, including the preferences and capabilities of terrorists. In many cases, it would be natural to represent uncertainty over these parameters using continuous distributions (such as uniform intervals or Gaussians). However, existing solution algorithms are limited to considering a small, finite number of possible attacker types with different payoffs. We introduce a general model of infinite Bayesian Stackelberg security games that allows payoffs to be represented using continuous payoff distributions. We then develop several techniques for finding approximate solutions for this class of games, and show empirically that our methods offer dramatic improvements over the current state of the art, providing new ways to improve the robustness of security game models.