Explanation-Based Learning: Finding Better Explanations Via Partial Evaluation

基于解释的学习：通过部分评估找到更好的解释

• 批准号: 9211045
• 负责人: Oren Etzioni
• 金额: $ 6万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-07-01 至 1994-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9211045&HistoricalAwards=false
• 关键词: Explanation; Based; Learning; Finding; Better

Controlling search is a central concern for AI. Overcoming combinatorial search in realistic planning, design, and reasoning problems requires large doses of domain specific search control knowledge. Explanation Based Learning (EBL) has emerged as a standard technique for acquiring search control knowledge. Previous EBL work has produced impressive demonstrations but has also uncovered a fundamental problem EBL frequently constructs overlycomplex explanations that yield ineffective control knowledge. This research describes a solution: integrating EBL with partial evaluation to improve EBL's explanations. In standard EBL systems, the problem solver's behavior on a training example determines what EBL explains and how. Partial evaluation, in contrast, performs a global analysis that often yields simpler and more general explanations. In previous work, STATIC (a partial evaluator written by the PI) was pitted against PRODIGY/EBL, a state of the art EBL system. When tested in PRODIGY/EBL's benchmark problem spaces, STATIC generated search control knowledge that was up to three times a effective as PRODIGY/EBL's, and did so twenty six to seventy seven times faster. Since STATIC's analysis in not focused by training examples, however, it may flounder when confronted with large and complex problem spaces. The PI intends to design and build a hybrid system , called DYNAMIC, that will overcome the weaknesses of both approaches. DYNAMIC will identify learning opportunities a la PRODIGY/EBL, BUT GENERATE EXPLANATIONS a la STATIC. The detailed studies of the two systems suggest that DYNAMIC will significantly out perform both, and yield insights in two fundamental questions: how to improve machine generated explanations, and what is the appropriate role of training examples in explanation based learning? //

控制搜索是AI的核心问题。 在现实的计划，设计和推理问题中克服组合搜索需要大量域特定搜索控制知识。 基于解释的学习（EBL）已成为获取搜索控制知识的标准技术。 以前的EBL工作​​已经产生了令人印象深刻的示威活动，但也发现了一个基本问题EBL经常构建过度复杂的解释，从而产生无效的控制知识。 这项研究描述了一种解决方案：将EBL与部分评估集成，以改善EBL的解释。 在标准的EBL系统中，问题解决者在培训示例中的行为确定了EBL的解释以及如何解释。 相比之下，部分评估进行了全球分析，通常会产生更简单，更一般的解释。 在先前的工作中，静态（PI撰写的部分评估者）与神童/EBL（最先进的EBL系统的状态）相提并论。 当在Prodigy/EBL的基准问题空间中进行测试时，静态生成的搜索控制知识的生效最多是Prodigy/EBL的三倍，并且速度持续了二十六至77倍。 但是，由于静态的分析不是通过训练示例而集中的，因此当面对大型且复杂的问题空间时，它可能会比较挣扎。 PI打算设计和建立一个称为动态的混合系统，该系统将克服两种方法的弱点。 动态将确定学习机会是神童/EBL，但会产生解释。 对这两个系统的详细研究表明，动态将显着执行，并在两个基本问题中产生见解：如何改善机器生成的解释，以及培训示例在基于解释的学习中的适当作用是什么？ //