绿色安全游戏与社区参与
Green Security Game with Community Engagement
摘要：
虽然已经开发了博弈论模型和算法来打击绿色安全领域的非法活动，如偷猎和过度捕捞，但现有的工作都没有考虑社区参与的关键方面：社区成员被执法部门招募为线人，可以提供有价值的提示，例如，正在进行的非法活动的地点，以协助巡逻。我们填补了这一空白，并（i）引入了一种新的两阶段安全博弈模型的社区参与，与二分图表示的告密者-攻击者的社会网络和一个水平- $κ$响应模型的启发，认知层次;（ii）提供复杂性的结果和精确，近似和启发式算法选择告密者和分配巡逻对水平- $κ$（$κ<\infty$）攻击者;（iii）提供了一种新的算法来找到针对级别$\infty$攻击者的最优防御策略，该算法将优化参数化不动点的问题转换为双层优化问题，其中内层只是线性规划，外层只有线性数量的变量和单个线性约束。我们还通过大量的实验来评估算法。
英文摘要：
While game-theoretic models and algorithms have been developed to combat illegal activities, such as poaching and over-fishing, in green security domains, none of the existing work considers the crucial aspect of community engagement: community members are recruited by law enforcement as informants and can provide valuable tips, e.g., the location of ongoing illegal activities, to assist patrols. We fill this gap and (i) introduce a novel two-stage security game model for community engagement, with a bipartite graph representing the informant-attacker social network and a level-$κ$ response model for attackers inspired by cognitive hierarchy; (ii) provide complexity results and exact, approximate, and heuristic algorithms for selecting informants and allocating patrollers against level-$κ$ ($κ<\infty$) attackers; (iii) provide a novel algorithm to find the optimal defender strategy against level-$\infty$ attackers, which converts the problem of optimizing a parameterized fixed-point to a bi-level optimization problem, where the inner level is just a linear program, and the outer level has only a linear number of variables and a single linear constraint. We also evaluate the algorithms through extensive experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
