Adversarial patrolling with reactive point processes
Adversarial patrolling with reactive point processes
复制标题
具有反应点过程的对抗性巡逻
DOI：
--
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
R. Fitch
中科院分区：
文献类型：
--
作者：
Bn Hefferan;Oliver M. Cliff;R. Fitch
对抗性巡逻是一个算法问题，其中机器人访问给定区域内的站点，以便检测对手的存在。我们制定和解决这个问题的一个新的变种，入侵事件发生在离散的位置，并假设在时间上聚类。与相关配方不同，我们使用被称为反应点过程的随机点过程来模拟对手的行为，该过程自然地模拟了农业中的害虫入侵和杂草生长等时间自激事件。我们提出了一个渐近最优的，任何时候算法的基础上蒙特卡洛树搜索，计划的机器人的运动给出了一个单独的事件检测系统，以调节事件的传播在它访问的网站。我们说明了我们的算法在模拟使用几种情况下的行为，并比较其性能的割草机规划算法。我们的结果表明，我们的配方和解决方案是有前途的，使实际应用和进一步的理论扩展。
Adversarial patrolling is an algorithmic problem where a robot visits sites within a given area so as to detect the presence of an adversary. We formulate and solve a new variant of this problem where intrusion events occur at discrete locations and are assumed to be clustered in time. Unlike related formulations, we model the behaviour of the adversary using a stochastic point process known as the reactive point process, which naturally models temporally self-exciting events such as pest intrusion and weed growth in agriculture. We present an asymptotically optimal, anytime algorithm based on Monte Carlo tree search that plans the motion of a robot given a separate event detection system in order to regulate event propagation at the sites it visits. We illustrate the behaviour of our algorithm in simulation using several scenarios, and compare its performance to a lawnmower planning algorithm. Our re-sults indicate that our formulation and solution are promising in enabling practical applications and further theoretical extensions.