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Strategic Coordination of Human Patrollers and Mobile Sensors With Signaling for Security Games

AAAI 2018 · Learning theory and algorithms › Submodular and game algorithms · approach: Opponent modeling and subgame solving · 25 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add 4 UAV sensors to a single patrollerpatroller-only defender · 8-area cycle exampledefender expected utility: -17/4 -> -11/4Helped
add strategic signaling to UAVs (ablation)patroller + UAV allocation · 8-area cycle exampledefender expected utility: -11/4 -> -2Helped
balance patrollers and sensors rather than all patrollers or many sensors with few patrollers (ablation)SEG defender budget split · random games, 30 targets (Figure 2 left)defender utilityMixed
enable strategic signaling by sensors (ablation)SEG with 4 patrollers and 10 sensors · general-sum games, varying correlation (Figure 2 right)defender utilityHelped
use TailoredGreedy instead of MILP for the slave problem (ablation)column generation · slave problem, random weightsobjective valueHurt
use TailoredGreedy instead of MILP for the slave problem (ablation)column generation · zero-sum SEGsrunning time: at least 6 times fasterHelped
use branch and price (SEGer) instead of solving one LP per target (NtLP) (ablation)general-sum SEG solver · general-sum SEGsrunning timeHelped
use TailoredGreedy within SEGer (ablation)SEGer[milp] · general-sum SEGsrunning timeHelped

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