Refining Concepts And Domain Theories By Combining Explanation-Based And Empirical Learning

通过结合基于解释的学习和实证学习来完善概念和领域理论

• 批准号: 9102926
• 负责人: Raymond Mooney
• 金额: $ 10.63万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-09-01 至 1993-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9102926&HistoricalAwards=false
• 关键词: Refining; Concepts; Domain; Theories; Combining

Recently, two general approaches have emerged in machine learning research. Empirical learning involves inductively acquiring concept descriptions by examining the similarities and differences among a large number of examples. Explanation-based learning (EBL) involves using existing knowledge to explain and generalize single examples and thereby acquire operational concept descriptions. However, both of these approaches have important weaknesses. Standard empirical methods cannot take adequate advantage of existing knowledge. Standard explanation- based methods cannot deal with domain theories which are incomplete or incorrect. Our goal is to develop robust and efficient methods that combine explanation-based and empirical techniques to refine imperfect concepts and domain theories to account for a set of empirical data.

最近，机器学习研究中出现了两种通用方法。 实证学习涉及通过检查大量示例之间的异同来归纳获得概念描述。 基于解释的学习（EBL）涉及使用现有知识来解释和概括单个示例，从而获得可操作的概念描述。 然而，这两种方法都有重要的弱点。 标准的经验方法无法充分利用现有知识。 标准的基于解释的方法无法处理不完整或不正确的领域理论。 我们的目标是开发稳健而有效的方法，将基于解释的技术和经验技术相结合，以完善不完善的概念和领域理论，以解释一组经验数据。