A Decision Logic Approach to the Port-of-Entry Inspection Problem

入境口岸检查问题的决策逻辑方法

• 批准号: 0518543
• 负责人: Fred Roberts
• 金额: --
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-15 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0518543&HistoricalAwards=false
• 关键词: Decision; Logic; Approach; Port; Entry

Finding ways to intercept illicit nuclear materials and weapons destined for the U.S. via the maritime transportation system is an exceedingly difficult task. Today, only a small percentage of ships entering U.S. ports have their cargoes inspected. The purpose of this study is to develop decision support algorithms that will help us to optimally intercept illicit materials and weapons and to test the algorithms on real data arising from port-of-entry inspection. The algorithms sought will find inspection schemes that minimize total cost, including "cost" of false positives and false negatives. Imagine a stream of entities arriving at a port and a decision maker having to decide how to inspect them, which to subject to further inspection and which to allow to pass through with only minimal levels of inspection. This is a complex sequential decision making problem. The ultimate goal of an inspection scheme is to classify an entity into important categories and states. In the simplest case, there are two categories ("ok" and "suspicious") and two states ("present" or "absent"). This classification can be described as involving a boolean decision function. Different binary tree representations for a boolean decision function have different inspection costs associated with them and an optimal decision tree representation is sought. Modeling sensors used to test for attributes makes the problem more realistic and brings in possible inspection errors and false positives and negatives. Extending the problem to more than two categories and more than two states also makes the problem more realistic.The project will be carried out in collaboration between a university team and a team from the Los Alamos National Laboratory and will initially follow an approach pioneered at Los Alamos. Four graduatestudents will be key participants in the project and many others will be involved through associated public events. The topic lends itself well to undergraduate research and participating faculty will coordinate topics with an existing undergraduate research program that caters to students from all over the U.S. To broaden the impact of the project, a seminar series will be publicized to neighboring institutions and a public workshop will be held. The workshop will include talks of an expository/tutorial nature, making it appealing tonon-specialists and students. A project website and a project book will help to disseminate project results and ideas widely.

寻找方法拦截通过海上运输系统运往美国的非法核材料和武器是一项极其艰巨的任务。如今，只有一小部分进入美国港口的船舶接受了货物检查。本研究的目的是开发决策支持算法，帮助我们以最佳方式拦截非法材料和武器，并根据入境口岸检查产生的真实数据测试算法。所寻求的算法将找到最小化总成本的检查方案，包括误报和漏报的“成本”。 想象一下，一批实体抵达港口，决策者必须决定如何检查它们，哪些要接受进一步检查，哪些只需要最低限度的检查即可通过。这是一个复杂的顺序决策问题。检查方案的最终目标是将实体分为重要类别和状态。在最简单的情况下，有两个类别（“正常”和“可疑”）和两种状态（“存在”或“不存在”）。 这种分类可以描述为涉及布尔决策函数。布尔决策函数的不同二叉树表示具有与其相关的不同检查成本，并且寻求最佳决策树表示。对用于测试属性的传感器进行建模使问题更加现实，并带来可能的检查错误以及误报和漏报。将问题扩展到两个以上类别和两个以上状态也使问题更加现实。该项目将由大学团队和洛斯阿拉莫斯国家实验室的团队合作进行，最初将遵循洛斯阿拉莫斯国家实验室首创的方法阿拉莫斯。 四名研究生将成为该项目的主要参与者，许多其他人将通过相关的公共活动参与其中。该主题非常适合本科生研究，参与的教师将把主题与现有的本科生研究计划相协调，以满足来自美国各地的学生的需求。为了扩大该项目的影响力，将向邻近机构和公共研讨会宣传一系列研讨会将举行。该研讨会将包括说明性/教程性质的演讲，使其对非专家和学生有吸引力。项目网站和项目手册将有助于广泛传播项目成果和想法。