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Incremental Learning via Exceptions for Agents and Humans: Evaluating KR Comprehensibility and Usability

机译：通过例外学习代理和人类：评估KR可理解性和可用性

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摘要

Acquiring knowledge directly from the domain expert requires a knowledge representation and specification method that is comprehensible and feasible for the holder and creator of that knowledge. The technique, known as multiple classification ripple down rules (MCRDR), is novelly applied to the problem of building and maintaining a library of training scenarios for use by customs and immigration officer trainees in our agent-based virtual environment which may be indexed for retrieval based on the rules associated with them. Our evaluation study aims to demonstrate the utility of the MCRDR combined case and exception structure rule-based approach over standard rules alone and a non-case-based approach.
机译：直接从领域专家获取知识需要了解知识表示和规范方法，这对于该知识的持有人和创造者来说是可观的和可行的。称为多个分类纹波规则（MCRDR）的技术是新颖的应用于建设和维护了在我们基于代理的虚拟环境中的海关和移民官员学员的培训方案的问题，这些虚拟环境可能被索引用于检索根据与他们相关的规则。我们的评估研究旨在展示MCRDR组合案例和异常结构规则的方法对单独的标准规则和基于非案例的方法的实用性。

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