NSF Young Investigator: New Directions in Computational Learning Theory

构建从经验中学习的机器是人工智能的一个重要研究目标。近年来，机器学习的理论研究受到了广泛的关注。该项目侧重于几个新的方向，旨在增强当前的学习模型，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存在一个定义明确的边界，将所有对象划分为概念的实例和非实例。然而，在现实中，对象的分类往往不那么明确：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”或有点像“9”的手写字符。在这些情况下，学习者的一个可能目标是确定哪些对象是不可分类的（即分类不明确），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一个分类，这样就不会对对象进行错误的分类。因此，对于第二个目标，学习器可以任意地对那些可能是双向的对象进行分类。这个项目定义并研究了这两种可能性的学习模型。此外，该项目继续PI的工作，即开发和研究正式的教学模式，以了解了解目标概念和学习者的教师如何减少学习者所需的培训时间。除了具有理论意义外，该研究在改善自动化制造环境方面也有潜在的应用。

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.