CAREER: Object Identification in Intelligent Systems

对象识别-决定两个观察到的对象实际上是同一个对象的任务-是任何智能系统或智能体的基本要求。 近年来，基于贝叶斯框架开发了一种解决这个问题的方法，该框架确定了对象在随后的观察中的预期外观的概率，给定其当前外观。 该理论已成功地应用于识别汽车的任务，在高速公路网络中广泛分离的网站的摄像机观察。本研究将建立和扩展涉及对象识别和数据关联的理论结果，并将展示如何将这些结果应用于现实世界的问题。 一个目标是整合和推广工作的数据关联社区，专注于跟踪独立移动的对象，其状态可以由多变量高斯模型，与工作的不确定性在人工智能社区，它产生了各种紧凑的状态表示和相关的推理算法。 我们的目标是提出一个统一的框架，具有更广泛的适用性比目前的理论。 第二个目标是开发改进的启发式算法，用于近似理论应用于现实世界领域时出现的棘手问题的解决方案。 我们的目标是开发新的算法，并证明改进的性能超过现有的。第三个目标是将该理论应用于其他现实任务，例如保持一致性和消除数据库中的重复条目。 我们的目标是说明从独立领域理论到特定领域问题的推理模式。这些研究活动构成了人工智能社区更广泛努力的一部分，即开发模型和算法，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的教育计划将加强计算机科学计划，特别是在米德尔伯里学院，并在一般文科院校，在两个方面：通过创新，跨学科的方法来介绍计算机科学概念，以提高一年级和二年级的大学生在这个领域的兴趣，当他们中的许多人可能会停止在数学或科学课程;并通过学生早期参与本科生研究项目。 该项目的最终目标是为其他人提供一个模式，以整合本科文科院校的研究和教育活动。

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.