Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
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
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
Computing optimal randomized resource allocations for massive security games
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Multi-agent patrolling: an empirical analysis of alternative architectures
MABS'02 Proceedings of the 3rd international conference on Multi-agent-based simulation II
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
Security and Game Theory: Algorithms, Deployed Systems, Lessons Learned
Security and Game Theory: Algorithms, Deployed Systems, Lessons Learned
Using multi-agent simulation to improve the security of maritime transit
MABS'11 Proceedings of the 12th international conference on Multi-Agent-Based Simulation
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
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Despite recent successful real-world deployments of Stackelberg Security Games (SSGs), scale-up remains a fundamental challenge in this field. The latest techniques do not scale-up to domains where multiple defenders must coordinate time-dependent joint activities. To address this challenge, this paper presents two branch-and-price algorithms for solving SSGs, SMARTO and SMARTH, with three novel features: (i) a column-generation approach that uses an ordered network of nodes (determined by solving the traveling salesman problem) to generate individual defender strategies; (ii) exploitation of iterative reward shaping of multiple coordinating defender units to generate coordinated strategies; (iii) generation of tighter upper-bounds for pruning by solving security games that only abide by key scheduling constraints. We provide extensive experimental results and formal analyses.