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Continuous Search and Patrolling on Networks

网络上的持续搜索和巡逻

基本信息

批准号：
1935826
负责人：
Thomas Lidbetter
金额：
$ 27.66万
依托单位：
Rutgers University Newark
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935826&HistoricalAwards=false
关键词：
Continuous Search Patrolling Networks

项目摘要

This award will contribute to securing the national defense by modeling and solving search and patrolling problems on networks. The project will address how to optimally patrol an airport or shopping mall to minimize the risk of a terrorist attack, how to patrol a border to guard against infiltration, or how to optimally search for an improvised explosive device or lost hiker. A major challenge of modeling such problems is that intelligent adversaries may have the capability to view current search or patrolling policies and exploit their weaknesses. This award supports an improved understanding of the strategic nature of search and patrolling problems, so that better policies can be employed to improve public safety and security. It will also address the need to understand how search and patrolling policies may be constrained by the topology of the environment. This award will support the participation of a talented graduate student in this research, and the PI will integrate the results of the research into a graduate level course in game theory.This research models search and patrolling problems on a network in continuous time and space, rather than the approach taken by most previous work of applying finite methods to a discretized search space. The project will consider the problems of finding (i) a patrol of a network that minimizes the probability of a successful attack or infiltration by an intelligent adversary and (ii) a time-minimal search for a target hidden on a network according to either a known or unknown probability distribution. A game theoretic framework will be used to deal with adversaries and unknown probability distributions, whilst a "one-sided" approach will be used in the case of known probability distributions. The research will exploit graph theoretical properties of networks to produce optimal or near-optimal policies based on an understanding of the structure of the networks, rather than using black box algorithms.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项将通过建模和解决网络搜索和巡逻问题来为确保国防做出贡献。该项目将解决如何以最佳方式巡逻机场或购物中心，以最大限度地降低恐怖袭击的风险，如何在边境巡逻以防止渗透，或者如何以最佳方式搜寻简易爆炸装置或失踪的徒步旅行者。对此类问题进行建模的一个主要挑战是，聪明的对手可能有能力查看当前的搜索或巡逻策略并利用其弱点。该奖项支持加深对搜索和巡逻问题的战略性质的理解，以便采取更好的政策来改善公共安全。它还将解决了解搜索和巡逻策略如何受到环境拓扑限制的需要。该奖项将支持一名才华横溢的研究生参与这项研究，PI将把研究成果整合到博弈论研究生水平课程中。这项研究对连续时间和空间的网络上的搜索和巡逻问题进行建模，而不是像以前大多数工作那样将有限方法应用于离散搜索空间。该项目将考虑以下问题：(i) 网络巡逻，最大限度地减少智能对手成功攻击或渗透的概率；(ii) 根据已知或未知的概率分布，以最短时间搜索隐藏在网络上的目标。博弈论框架将用于处理对手和未知的概率分布，而“单方面”方法将用于已知概率分布的情况。该研究将利用网络的图论特性，根据对网络结构的理解，而不是使用黑盒算法来制定最佳或接近最佳的政策。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。