Presidential Young Investigator Award (Computer and Information Science)

机器学习程序是一种随着时间的推移而提高其性能的程序，通常是通过获取有关其正在执行的任务的新知识。本研究旨在提高我们对构建机器学习程序的基础和实践方面的理解。重点在三个方面：(a)寻找学习程序可以使用它们已经拥有的知识来学习额外知识的方法，(b)理解主动实验在帮助学习过程中的作用，(c)分析不同表示语言对学习过程的限制。为了在这些领域取得进展，我们进行了对照实验来比较不同的学习方法、实验策略和表示语言。本研究的重要性在于，软件系统和专家系统的生产和调试是使用计算机的主要成本——相比之下，硬件和人员成本很小。机器学习技术具有显著增强计算机使用方式的潜力。我们不愿意一步一步地给计算机编程，而愿意为所期望的任务给计算机一个近似的、不完整的、低效的程序。这个近似的、不完整的、低效的程序可以通过使用机器学习技术进行调试、完善，并自动提高效率。计算机可以被有效地教授，而不是费力地编程。

A machine learning program is a program that improves its performance over time, usually by acquiring new knowledge about the tasks it is performing. This research seeks to improve our understanding of both the foundations and the practical aspects of building machine learning programs. The focus is in three directions: (a) finding ways by which learning programs can use the knowledge they already have to learn additional knowledge, (b) understanding the role of active experimentation in aiding the learning process, and (c) analyzing the constraints that different representation languages place on the learning process. To make progress in each of these areas, controlled experiments are conducted to compare different learning methods, experimentation strategies, and representation languages. The importance of this research lies in the fact that the production and debugging of software systems and expert systems is the principal cost of using computers - hardware and personnel costs are small in comparison. Machine learning techniques hold the potential for significantly enhancing the way computers can be used. Rather than programming the computer, step by step, we would like to give the computer an approximate, incomplete, and inefficient procedure for a desired task. This approximate, incomplete, and inefficient program could then be debugged, perfected, and made more efficient automatically, through the use of machine learning techniques. Computers could be efficiently taught, rather than laboriously programmed.