"Explanation-Based Learning"

“基于解释的学习”

• 批准号: 8719766
• 负责人: Gerald DeJong
• 金额: $ 22.35万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-06-01 至 1992-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8719766&HistoricalAwards=false
• 关键词: Explanation; Based; Learning

This research continues work on knowledge-intensive systems for machine learning. The approach taken, Explanation Based Learning, is expanded from small "clean" problems here into more complex domains. In such complex domains, an automated learning system will inevitably acquire incorrect concepts. Inference will not be logically complete, but only plausible. Research is targeted toward detecting and refining such concepts when they cause performance failures. The significance of this work is that it expands machine learning capabilities attack more realistic problems, and offers a promising avenue of solution to the problem of knowledge acquisition in expert systems, since an EBL system derives its own "rules" rather than needing them to be specified and programmed by a human intermediary.

这项研究继续致力于机器学习的知识密集型系统。采用的方法，基于解释的学习，从小的“干净”问题扩展到更复杂的领域。在如此复杂的领域中，自动学习系统不可避免地会获得错误的概念。推论不会在逻辑上是完整的，而只是似是而非的。研究的目标是在这些概念导致性能故障时检测和改进它们。这项工作的意义在于，它扩展了机器学习能力来解决更现实的问题，并为专家系统中的知识获取问题提供了一条有前途的解决方案，因为EBL系统派生出自己的“规则”，而不是需要由人类中介指定和编程。