NSF Young Investigator: New Directions in Computational Learning Theory

NSF 青年研究员：计算学习理论的新方向

• 批准号: 9357707
• 负责人: Sally Goldman
• 金额: $ 31.25万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-07-15 至 1999-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9357707&HistoricalAwards=false
• 关键词: NSF; Young; Investigator; New; Directions

Building machines that learn from experience is an important research goal of artificial intelligence. Recently, considerable research attention has been devoted to the theoretical study of machine learning. This project focuses on several new directions designed to enhance the current learning models to more accurately model real-life learning situations. Most work in the area of concept learning assumes there is a well-defined border that divides all objects into those that are instances of the concept and those that are not. In reality, though, categorization of objects is often not so clear cut: an algorithm designed to read handwritten cheques will likely encounter many handwritten characters that look somewhat like a `4,` and somewhat like a `9.` In these situations, one possible goal for the learner is to determine which objects are unclassifiable (i.e., the classification is not clear cut) as well as determining the classifications of objects which are classifiable. Another possibility is to require only that the learner determine a categorization so that no object is incorrectly categorized. Thus, for this second goal, the learner can arbitrarily categorize those objects that can go either way. This project defines and studies learning models for both possibilities. Also this project continues the PI's work initiated on developing and studying formal models of teaching to understand how a teacher with knowledge of the target concept and the learner can reduce the training time needed by the learner. As well as being of theoretical interest, there are potential applications of the research to improving automated manufacturing environments.

制造从经验中学习的机器是人工智能的重要研究目标。近年来，机器学习的理论研究受到了广泛的关注。本项目侧重于几个新的方向，旨在增强现有的学习模型，以更准确地模拟现实生活中的学习情况。概念学习领域的大多数工作都假设有一个定义良好的边界，将所有对象分为那些是概念实例的对象和那些不是概念实例的对象。然而，在现实中，对象的分类往往不是那么明确：设计用于读取手写支票的算法可能会遇到许多看起来有点像`4‘、有点像`9’的手写字符。在这些情况下，学习者的一个可能的目标是确定哪些对象是不可分类的(即，分类不是明确的)以及确定哪些对象是可分类的。另一种可能性是只要求学习者确定分类，这样就没有对象被错误地分类。因此，对于第二个目标，学习者可以任意地将这些对象归类为任何一种。这个项目定义并研究了这两种可能性的学习模式。此外，该项目还继续了PI关于开发和研究正式教学模式的工作，以了解掌握目标概念和学习者知识的教师如何减少学习者所需的培训时间。除了理论上的兴趣，这项研究在改善自动化制造环境方面也有潜在的应用。