CAREER: Object Identification in Intelligent Systems

职业：智能系统中的对象识别

• 批准号: 9876181
• 负责人: Timothy Huang
• 金额: $ 20万
• 依托单位: Middlebury College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-05-01 至 2005-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9876181&HistoricalAwards=false
• 关键词: CAREER; Object; Identification; Intelligent; Systems

Object identification -- the task of deciding that two observed objects are in fact one and the same object -- is a fundamental requirement for any intelligent system or situated agent that reasons about individuals. An approach to this problem has been developed in recent years based on a Bayesian framework that determines the probability for an object's expected appearance at subsequent observations, given its current appearance. The theory has been successfully applied to the task of recognizing cars observed by cameras at widely separated sites in a freeway network. This research will build upon and extend theoretical results involving object identification and data association, and will show how to apply these results to real-world problems.This research has three primary thrusts. One aim is to integrate and generalize work from the data association community, which has focused on tracking independently moving objects whose state can be modeled by multivariate Gaussians, with work from the uncertainty in artificial intelligence community, which has produced a variety of compact state representations and associated inference algorithms. The goal is to present a unified framework with broader applicability than current theory. A second aim is to develop improved heuristic algorithms for approximating solutions to intractable problems that arise when the theory is applied to real-world domains. The goal is to develop new algorithms and demonstrate improved performance over existing ones. The third aim is to apply the theory to other real-world tasks, such as maintaining consistency and eliminating duplicate entries in databases. The goal is to illustrate the patterns of reasoning involved in moving from domain-independent theory to domain-specific problems. These research activities form part of a more general effort within the AI community to develop models and algorithms for reasoning in uncertain environments with noisy and imprecise sensors. The PI's education plans will strengthen the computer science program at Middlebury College in particular, and at liberal arts institutions in general, in two ways: through innovative, inter-disciplinary approaches to presenting introductory computer science concepts so as to increase the interest among first and second year college students in this field at a time when many of them might otherwise stop taking courses in mathematics or science; and through early involvement of students in undergraduate research projects. The project's ultimate goal is to provide a model for others to follow in ways to integrate research and education activities at undergraduate liberal arts institutions.

对象识别——确定两个观察到的对象实际上是同一个对象的任务——是任何智能系统或推理个体的情境代理的基本要求。 近年来，基于贝叶斯框架开发了解决此问题的方法，该框架确定物体在给定当前外观的情况下在后续观察中预期出现的概率。 该理论已成功应用于识别高速公路网络中相隔较远的地点的摄像机观察到的汽车的任务。这项研究将建立在并扩展涉及对象识别和数据关联的理论成果的基础上，并将展示如何将这些结果应用于现实世界的问题。这项研究有三个主要目标。 目标之一是整合和概括数据关联社区的工作，该社区的工作重点是跟踪独立移动的物体，其状态可以通过多元高斯模型进行建模，以及人工智能社区的不确定性工作，该社区产生了各种紧凑的状态表示以及相关的推理算法。 目标是提出一个比当前理论具有更广泛适用性的统一框架。 第二个目标是开发改进的启发式算法，以近似解决理论应用于现实世界时出现的棘手问题。 目标是开发新算法并展示比现有算法改进的性能。第三个目标是将理论应用于其他现实世界的任务，例如保持一致性和消除数据库中的重复条目。 目标是说明从领域无关理论转向特定领域问题所涉及的推理模式。这些研究活动是人工智能社区内更普遍努力的一部分，旨在开发模型和算法，以便在带有噪声和不精确传感器的不确定环境中进行推理。 PI 的教育计划将通过两种方式加强米德尔伯里学院和整个文理学院的计算机科学项目：通过创新的跨学科方法来介绍计算机科学入门概念，以提高第一和第二人的兴趣。该领域的二年级大学生，否则他们中的许多人可能会停止学习数学或科学课程； 并通过学生早期参与本科研究项目。 该项目的最终目标是为其他人提供一个可以效仿的模式，以整合本科文科院校的研究和教育活动。