图 书 馆
保护野生生物的博弈论Game Theory to Protect Wild Life |
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| 课程网址: | http://videolectures.net/NTUcomplexity2017_an_game_theory_wild_li... |
| 主讲教师: | Bo An |
| 开课单位: | 南洋理工大学 |
| 开课时间: | 2017-04-03 |
| 课程语种: | 英语 |
| 中文简介: |  偷猎对关键物种和整个生态系统的保护构成严重威胁。尽管在许多国家/地区进行步行巡逻是防止偷猎的最常用方法，但这种巡逻通常无法充分利用有限的巡逻资源。为了解决这种情况，提出了PAWS（野生动物安全保护助手）作为博弈论决策辅助工具，以优化巡逻资源的使用。该演讲报告了PAWS从拟议的决策帮助到定期部署的应用程序的重大演变，报告了从2014年春季在非洲进行的首次测试到后来的持续发展到目前在东南亚的常规使用以及未来计划的经验教训全球部署。我们概述了导致PAWS定期部署的关键技术进步：（i）在生成巡逻路线时纳入了复杂的地形特征（例如山脊线）； （ii）处理物种分布的不确定性（博弈论收益）； （iii）确保在细粒度指导下巡逻大型保护区的可扩展性； （iv）处理复杂的巡逻调度约束。 |
| 课程简介: | Poaching is a serious threat to the conservation of key species and whole ecosystems. While conducting foot patrols is the most commonly used approach in many countries to prevent poaching, such patrols often do not make the best use of limited patrolling resources. To remedy this situation, PAWS (Protection Assistant for Wildlife Security) was proposed as a game-theoretic decision aid to optimize the use of patrolling resources. This talk reports on PAWS’s significant evolution from a proposed decision aid to a regularly deployed application, reporting on the lessons from the first tests in Africa in Spring 2014, through its continued evolution since then, to current regular use in Southeast Asia and plans for future worldwide deployment. We outline key technical advances that lead to PAWS’s regular deployment: (i) incorporating complex topographic features, e.g., ridgelines, in generating patrol routes; (ii) handling uncertainties in species distribution (game theoretic payoffs); (iii) ensuring scalability for patrolling large-scale conservation areas with fine-grained guidance; and (iv) handling complex patrol scheduling constraints. |
| 关 键 词: | 野生动物安全保护助手; 巡逻资源; 博弈论 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-22：cjy |
| 最后编审: | 2020-11-22：cjy |
| 阅读次数: | 47 |