Applications of artificial intelligence for wildlife protection have focused on learning models of poacher behavior based on historical patterns. However, poachers' behaviors are described not only by their historical preferences, but also their reaction to ranger patrols. Past work applying machine learning and game theory to combat poaching have hypothesized that ranger patrols deter poachers, but have been unable to find evidence to identify how or even if deterrence occurs. Here for the first time, we demonstrate a measurable deterrence effect on real-world poaching data. We show that increased patrols in one region deter poaching in the next timestep, but poachers then move to neighboring regions. Our findings offer guidance on how adversaries should be modeled in realistic game-theoretic settings.

中文翻译：

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实地博弈论：增加巡逻对阻止偷猎者的影响

人工智能在野生动物保护中的应用侧重于学习基于历史模式的偷猎者行为模型。然而，偷猎者的行为不仅可以通过他们的历史偏好来描述，还可以通过他们对护林员巡逻的反应来描述。过去将机器学习和博弈论应用于打击偷猎的工作假设护林员巡逻可以阻止偷猎者，但一直无法找到证据来确定威慑是如何发生的，甚至是否发生。在这里，我们第一次展示了对现实世界偷猎数据的可衡量的威慑效果。我们表明，在一个地区增加巡逻阻止了下一时间步的偷猎，但偷猎者随后会转移到邻近地区。我们的研究结果为如何在现实的博弈论环境中模拟对手提供了指导。