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Divide to Defend: Collusive Security Games.

, , , , , and . GameSec, volume 9996 of Lecture Notes in Computer Science, page 272-293. Springer, (2016)

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Becoming Good at AI for Good., , , , , , , , , and 6 other author(s). AIES, page 664-673. ACM, (2021)Don't Put All Your Strategies in One Basket: Playing Green Security Games with Imperfect Prior Knowledge., , , , and . AAMAS, page 395-403. International Foundation for Autonomous Agents and Multiagent Systems, (2019)Divide to Defend: Collusive Security Games., , , , , and . GameSec, volume 9996 of Lecture Notes in Computer Science, page 272-293. Springer, (2016)CAPTURE: A New Predictive Anti-Poaching Tool for Wildlife Protection., , , , , , , , , and 1 other author(s). AAMAS, page 767-775. ACM, (2016)Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations., , , , , , , , , and 4 other author(s). CoRR, (2019)Keeping Pace with Criminals: An Extended Study of Designing Patrol Allocation against Adaptive Opportunistic Criminals., , , , , , and . Games, 7 (3): 15 (2016)Where there's Smoke, there's Fire: Wildfire Risk Predictive Modeling via Historical Climate Data., , , and . AAAI, page 15309-15315. AAAI Press, (2021)Taking It for a Test Drive: A Hybrid Spatio-Temporal Model for Wildlife Poaching Prediction Evaluated Through a Controlled Field Test., , , , , , , , , and . ECML/PKDD (3), volume 10536 of Lecture Notes in Computer Science, page 292-304. Springer, (2017)SPECTRE: A Game Theoretic Framework for Preventing Collusion in Security Games (Demonstration)., , , , , and . AAMAS, page 1498-1500. ACM, (2016)Cloudy with a Chance of Poaching: Adversary Behavior Modeling and Forecasting with Real-World Poaching Data., , , , , , , , , and 1 other author(s). AAMAS, page 159-167. ACM, (2017)