Scaling-up security games with boundedly rational adversaries: a cutting-plane approach

Authors:
Rong Yang;Albert Xin Jiang;Milind Tambe;Fernando Ordóñez
Affiliations:
University of Southern California, Los Angeles;University of Southern California, Los Angeles;University of Southern California, Los Angeles;University of Chile, Santiago, Chile
Venue:
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Year:
2013

Citing 10
Cited 0

Branch-And-Price: Column Generation for Solving Huge Integer Programs

Operations Research
Computing the optimal strategy to commit to

EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Computing correlated equilibria in multi-player games

Journal of the ACM (JACM)
Computing optimal randomized resource allocations for massive security games

Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Stackelberg vs. Nash in security games: interchangeability, equivalence, and uniqueness

Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Robust solutions to Stackelberg games: Addressing bounded rationality and limited observations in human cognition

Artificial Intelligence
Security and Game Theory: Algorithms, Deployed Systems, Lessons Learned

Security and Game Theory: Algorithms, Deployed Systems, Lessons Learned
Improving resource allocation strategy against human adversaries in security games

IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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
Computing optimal strategy against quantal response in security games

Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2

Quantified Score

Hi-index	0.00

Visualization

Abstract

To improve the current real-world deployments of Stackelberg security games (SSGs), it is critical now to efficiently incorporate models of adversary bounded rationality in large-scale SSGs. Unfortunately, previously proposed branch-and-price approaches fail to scale-up given the non-convexity of such models, as we show with a realization called COCOMO. Therefore, we next present a novel cutting-plane algorithm called BLADE to scale-up SSGs with complex adversary models, with three key novelties: (i) an efficient scalable separation oracle to generate deep cuts; (ii) a heuristic that uses gradient to further improve the cuts; (iii) techniques for quality-efficiency tradeoff.