论文标题
地面上的游戏理论：增加巡逻对威胁偷猎者的影响
Game Theory on the Ground: The Effect of Increased Patrols on Deterring Poachers
论文作者
论文摘要
人工智能在野生动植物保护中的应用集中在基于历史模式的偷猎行为模型上。但是，偷猎者的行为不仅是通过其历史偏好来描述的，而且还描述了他们对游侠巡逻的反应。过去的工作将机器学习和游戏理论用于打击偷猎，假设Ranger巡逻会阻止偷猎者，但无法找到证据来确定如何甚至发生威慑。在这里，我们第一次演示了对现实偷猎数据的可测量威慑作用。我们表明，在一个地区的一个地区，巡逻队增加了下一个时间步长，但偷猎者随后搬到邻近地区。我们的发现提供了有关如何在现实的游戏理论设置中建模对手的指导。
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.
