A knowledge-level analysis of explanation-based learning

M. Numao, M. Shimura
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引用次数: 3

Abstract

Although Explanation-Based Learning (EBL) has up to now been used only for deductive learning that improves execution speed, chunking in Soar, which is closely related to EBL, was demonstrated to acquire new knowledge. We first analyze such knowledge level learning in EBL, by showing that a rule set is specialized when rules in it are replaced by their composition, and is generalized when a rule is replaced by its decomposition. Counting on this discussion, we propose a method to learn generalized rules by making a decomposition of instances. Since this method acquires knowledge that is deduced from domain theory and induced from instances, it is a natural method for combining empirical and explanation-based learning. We demonstrate deductive and inductive aspects of our method by examples of logic circuit design and geometric analogy.
基于解释的学习的知识层面分析
尽管迄今为止,基于解释的学习(EBL)仅用于提高执行速度的演绎学习,但与EBL密切相关的Soar中的分块被证明可以获得新知识。我们首先分析了EBL中的这种知识水平学习,表明当规则集中的规则被它们的组合所取代时,规则集是专门化的,当规则被其分解所取代时,规则集是泛化的。在此基础上,我们提出了一种通过实例分解来学习广义规则的方法。由于该方法获得的知识是从领域理论中推导出来的,从实例中归纳出来的,因此它是一种将经验学习与基于解释的学习相结合的自然方法。我们通过逻辑电路设计和几何类比的例子来说明我们方法的演绎和归纳方面。
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