Research map / Learning theory and algorithms / Submodular and game algorithms
One Size Does Not Fit All: A Game-Theoretic Approach for Dynamically and Effectively Screening for Threats
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use marginal-based LP (MSLP/MGA) instead of column generation | TSG solver · random TSG instances | runtime | Helped |
| use marginal-based MGA instead of column generation with 1000-iteration cutoff | TSG solver · random TSG instances | screener utility | Helped |
| guarantee implementability via MGA instead of plain MSLP marginals | MSLP · random TSG instances | screener utility | No clear effect |
| categorize screenees by risk level and flight (TSG-RF) instead of risk level only (TSG-R) | TSG · random TSG instances | screener utility | Helped |
| categorize screenees by risk level and flight instead of one uniform category (TSG-U) | TSG · random TSG instances | screener utility | Helped |
Closest papers by meaning
- Solving Online Threat Screening Games using Constrained Action Space Reinforcement Learning (AAAI 2020 · similarity 0.88)
- Security Games on a Plane (AAAI 2017 · similarity 0.86)
- Non-Additive Security Games (AAAI 2017 · similarity 0.85)
- GUARD: Constructing Realistic Two-Player Matrix and Security Games for Benchmarking Game-Theoretic Algorithms (NeurIPS 2025 · similarity 0.84)
- Security Games with Layered Defenses: Adaptive Adversaries and Gittins Indices (AAAI 2026 · similarity 0.84)
- Strategic Coordination of Human Patrollers and Mobile Sensors With Signaling for Security Games (AAAI 2018 · similarity 0.84)
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