"Explanation-Based Learning"

这项研究继续研究机器学习的知识密集型系统。所采取的方法，基于解释的学习，从这里的小的“干净的”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。推理在逻辑上是不完整的，但只是看似合理的。研究的目标是在这些概念导致性能故障时对其进行检测和提炼。这项工作的意义在于，它扩展了机器学习能力，解决了更现实的问题，并为专家系统中的知识获取问题提供了一条有希望的解决途径，因为EBL系统推导出自己的“规则”，而不需要由人类中介指定和编程。

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.