CAREER: Object Identification in Intelligent Systems

物体识别--确定两个观察到的物体实际上是一个相同的物体--是对任何智能系统或关于个人的推理的情境代理的基本要求。近年来，基于贝叶斯框架开发了一种解决这个问题的方法，该框架在给定对象当前外观的情况下，确定对象在后续观测中预期出现的概率。该理论已经成功地应用于识别高速公路网中相隔很远的地点的摄像头拍摄到的汽车的任务。这项研究将建立和扩展涉及对象识别和数据关联的理论结果，并展示如何将这些结果应用于现实世界的问题。本研究有三个主要方面。一个目标是整合和推广来自数据关联社区的工作，该社区专注于跟踪独立运动的对象，其状态可以由多变量高斯建模，以及来自人工智能社区的不确定性的工作，该社区产生了各种紧凑的状态表示和相关的推理算法。其目标是提出一个统一的框架，比目前的理论具有更广泛的适用性。第二个目标是开发改进的启发式算法，用于近似解决当该理论应用于现实世界领域时出现的棘手问题的解决方案。目标是开发新的算法，并证明比现有算法性能更好。第三个目标是将该理论应用于其他现实世界的任务，如维护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到领域特定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广泛努力的一部分，目的是开发模型和算法，用于在具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特别是米德尔伯里学院和一般文科机构的计算机科学课程：通过创新的跨学科方法介绍计算机科学入门概念，以增加一年级和二年级大学生对该领域的兴趣，因为他们中的许多人可能会停止学习数学或科学课程；以及通过让学生及早参与本科生的研究项目。该项目的最终目标是提供一个范本，供其他人效仿，将本科文科院校的研究和教育活动整合在一起。

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.