Optimal Patrol Planning for Green Security Games with Black-Box Attackers
Optimal Patrol Planning for Green Security Games with Black-Box Attackers
复制标题
黑盒攻击者绿色安全博弈的最优巡逻规划
DOI：
10.1007/978-3-319-68711-7_24
复制
发表时间：
2017
期刊：
影响因子：
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通讯作者：
Joshua Mabonga
中科院分区：
文献类型：
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作者：
Haifeng Xu;Benjamin J. Ford;Fei Fang;B. Dilkina;A. Plumptre;Milind Tambe;M. Driciru;F. Wanyama;A. Rwetsiba;Mustapha Nsubaga;Joshua Mabonga
受保护濒危动物问题的激励，人们对优化保护区保护巡逻规划的兴趣激增。以前在这些领域的努力主要集中在优化巡逻路线对一个特定的有限理性偷猎者的行为模型，描述偷猎者的选择区域进行攻击。然而，这些规划算法并不适用于其他偷猎预测模型，特别是那些复杂的机器学习模型，最近被证明比传统的基于有限理性的模型提供更好的预测。此外，以前的巡逻规划算法不处理的重要问题，偷猎者推断巡逻路线的部分监测护林员的运动。在本文中，我们提出了OPERA，一个通用的巡逻规划框架：（1）生成最佳的可实施的巡逻路线，对黑盒攻击，可以代表广泛的偷猎预测模型;（2）采用熵最大化，以确保生成的路线是更不可预测的和强大的偷猎者的部分监控。我们对来自乌干达伊丽莎白女王保护区（QEPA）的真实世界数据集的实验表明，OPERA比基准算法和QEPA护林员过去使用的路线具有更好的防御效用，更有效的区域覆盖率和更大的不可预测性。
Motivated by the problem of protecting endangered animals, there has been a surge of interests in optimizing patrol planning for conservation area protection. Previous efforts in these domains have mostly focused on optimizing patrol routes against a specific boundedly rational poacher behavior model that describes poachers’ choices of areas to attack. However, these planning algorithms do not apply to other poaching prediction models, particularly, those complex machine learning models which are recently shown to provide better prediction than traditional bounded-rationality-based models. Moreover, previous patrol planning algorithms do not handle the important concern whereby poachers infer the patrol routes by partially monitoring the rangers’ movements. In this paper, we propose OPERA, a general patrol planning framework that: (1) generates optimal implementable patrolling routes against a black-box attacker which can represent a wide range of poaching prediction models; (2) incorporates entropy maximization to ensure that the generated routes are more unpredictable and robust to poachers’ partial monitoring. Our experiments on a real-world dataset from Uganda’s Queen Elizabeth Protected Area (QEPA) show that OPERA results in better defender utility, more efficient coverage of the area and more unpredictability than benchmark algorithms and the past routes used by rangers at QEPA.