Features of Machine Learning and Other Topics in Foundations of Computing

该项目将有助于理解计算机如何通过隔离增量学习算法的特征来编程学习，从而在理论上增强其学习潜力。这些特征被选择为类似于人类学习的特征。例如，使用一个学习事件的结果来帮助下一个学习努力的学习设备将被考虑。对人类和机器来说，另一种强大的学习技巧是提问的能力。将调查算法的相对学习潜力，作为它们用来提出问题的语言。初步结果表明，查询语言越强大，提问设备的学习潜力越大。查询语言强度的参数，如量词的数量和量词的变化数量将被严格检查。当前的程序测试技术不能也不能精确地确定正确性。这些技术的目的是揭示程序中的错误；但是，如果没有发现错误，则无法衡量程序的可靠性。因此，不存在如何“接近”纠正被测试程序的概念。这项研究的目的是发展一种理论，据此，当一个程序通过给定的测试时，就有可能准确地说出它的可靠性。

This project will contribute toward the goal of understanding how a computer can be programmed to learn by isolating features of incremental learning algorithms that theoretically enhance their learning potential. The features are chosen to resemble features of human learning. For example, learning devices that use the results of one learning episode to aid in the next learning endeavor will be considered. Another powerful learning technique, for both humans and machines, is the ability to ask questions. Investigations into the relative learning potential of algorithms as the language they use to pose questions will be conducted. Preliminary results indicate that the more powerful the query language used, the greater the learning potential of the device asking questions. Parameters of query language strength such as the number of quantifiers and the number of alternations of quantifiers will be vigorously examined. Current program testing techniques do not and cannot determine correctness precisely. The purpose of these techniques is to reveal errors in a program; however, if no errors are found, no measure is given as to how reliable the program is. Therefore there is no notion of how "close" to correct the tested program is. The goal of this research is to develop a theory whereby it is possible to say precisely how reliable a program is when it passes a given test.