Introduction to Linear Optimization
Introduction to Linear Optimization
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
The impact of adversarial knowledge on adversarial planning in perimeter patrol
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
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
Approximate strategic reasoning through hierarchical reduction of large symmetric games
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Learning and Approximating the Optimal Strategy to Commit To
SAGT '09 Proceedings of the 2nd International Symposium on Algorithmic Game Theory
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
GUARDS and PROTECT: next generation applications of security games
ACM SIGecom Exchanges
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Game theory and human behavior: challenges in security and sustainability
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
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
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
Computing Stackelberg strategies in stochastic games
ACM SIGecom Exchanges
Planning and learning in security games
ACM SIGecom Exchanges
Game-theoretic question selection for tests
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The fastest known algorithm for solving General Bayesian Stackelberg games with a finite set of follower (adversary) types have seen direct practical use at the LAX airport for over 3 years; and currently, an (albeit non-Bayesian) algorithm for solving these games is also being used for scheduling air marshals on limited sectors of international flights by the US Federal Air Marshals Service. These algorithms find optimal randomized security schedules to allocate limited security resources to protect targets. As we scale up to larger domains, including the full set of flights covered by the Federal Air Marshals, it is critical to develop newer algorithms that scale-up significantly beyond the limits of the current state-of-the-art of Bayesian Stackelberg solvers. In this paper, we present a novel technique based on a hierarchical decomposition and branch and bound search over the follower type space, which may be applied to different Stackelberg game solvers. We have applied this technique to different solvers, resulting in: (i) A new exact algorithm called HBGS that is orders of magnitude faster than the best known previous Bayesian solver for general Stackelberg games; (ii) A new exact algorithm called HBSA which extends the fastest known previous security game solver towards the Bayesian case; and (iii) Approximation versions of HBGS and HBSA that show significant improvements over these newer algorithms with only 1--2% sacrifice in the practical solution quality.