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
Solving transition independent decentralized Markov decision processes
Journal of Artificial Intelligence Research
Computing optimal strategies to commit to in extensive-form games
Proceedings of the 11th ACM conference on Electronic commerce
Security and Game Theory: Algorithms, Deployed Systems, Lessons Learned
Security and Game Theory: Algorithms, Deployed Systems, Lessons Learned
Computing optimal strategy against quantal response in security games
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-theoretic patrol strategies for transit systems: the TRUSTS system and its mobile app
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Planning and learning in security games
ACM SIGecom Exchanges
Protecting moving targets with multiple mobile resources
Journal of Artificial Intelligence Research
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In recent years there has been extensive research on game-theoretic models for infrastructure security. In time-critical domains where the security agency needs to execute complex patrols, execution uncertainty(interruptions) affect the patroller's ability to carry out their planned schedules later. Indeed, experiments in this paper show that in some real-world domains, small fractions of execution uncertainty can have a dramatic impact. The contributions of this paper are threefold. First, we present a general Bayesian Stackelberg game model for security patrolling in dynamic uncertain domains, in which the uncertainty in the execution of patrols is represented using Markov Decision Processes. Second, we study the problem of computing Stackelberg equilibrium for this game. We show that when the utility functions have a certain separable structure, the defender's strategy space can be compactly represented, and we can reduce the problem to a polynomial-sized optimization problem. Finally, we apply our approach to fare inspection in the Los Angeles Metro Rail system. Numerical experiments show that patrol schedules generated using our approach outperform schedules generated using a previous algorithm that does not consider execution uncertainty.