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Evaluation of Predictive Models for Wildlife Poaching Activity through Controlled Field Test in Uganda.

, , , , , , , , , , and . AAAI Workshops, volume WS-18 of AAAI Technical Report, page 344-347. AAAI Press, (2018)

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Adversary Models Account for Imperfect Crime Data: Forecasting and Planning against Real-world Poachers., , , , , , , , , and 2 other author(s). AAMAS, page 823-831. International Foundation for Autonomous Agents and Multiagent Systems Richland, SC, USA / ACM, (2018)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)Evaluation of Predictive Models for Wildlife Poaching Activity through Controlled Field Test in Uganda., , , , , , , , , and 1 other author(s). AAAI Workshops, volume WS-18 of AAAI Technical Report, page 344-347. AAAI Press, (2018)Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations., , , , , , , , , and 4 other author(s). CoRR, (2019)Optimal Patrol Planning for Green Security Games with Black-Box Attackers., , , , , , , , , and 1 other author(s). GameSec, volume 10575 of Lecture Notes in Computer Science, page 458-477. Springer, (2017)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)