Acquiring first-order knowledge about air traffic control

Y. Kodratoff, Christel Vrain
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引用次数: 15

Abstract

Abstract This paper presents an application of knowledge intensive generalization to knowledge acquisition, in the domain of air traffic control. We explain why knowledge intensiveness and first-order logic are sometimes necessary, as for instance in the application field studied here. An obvious advantage of first-order logic is its power of expression, while an obvious drawback is long computation time. We also describe some less obvious advantages and drawbacks of first-order logic, especially when the knowledge must be expressed as Horn clauses to retain some computational efficiency. Finally, we emphasize the large translation problem that must be solved in order to allow an efficient interaction with the expert. Two translation phases are necessary. One goes from the expert's language to Horn clauses, the second one goes back from Horn clauses to the expert's language. The first one is necessary to ensure automatic learning, while the second one allows the expert to understand what has been learned. Both phases are far from trivial and ask for choices that must be made carefully in order to avoid losing significant information. One of our unexpected results is that the second translation phase plays the role of a validation step. It thus becomes a very efficient way to acquire knowledge the expert has problems formalizing. Using first-order logic does complicate things, but it provides, as a reward, a powerful way of extracting and validating the acquired knowledge, especially when the field expert is unable to express his knowledge in a simple way.
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获取有关空中交通管制的一阶知识
摘要本文提出了知识密集型泛化在空中交通管制领域知识获取中的应用。我们解释了为什么知识密集和一阶逻辑有时是必要的,例如在这里研究的应用领域。一阶逻辑的一个明显的优点是它的表达能力,而一个明显的缺点是计算时间长。我们还描述了一阶逻辑的一些不太明显的优点和缺点,特别是当知识必须表示为Horn子句以保持一定的计算效率时。最后,我们强调必须解决的大型翻译问题,以便与专家进行有效的互动。两个翻译阶段是必要的。一个是从专家的语言转到霍恩从句,另一个从霍恩从句转回专家的语言。第一个是确保自动学习所必需的,而第二个是让专家理解所学的内容。这两个阶段都不是微不足道的,并且要求必须谨慎做出选择,以避免丢失重要信息。我们意想不到的结果之一是,第二个翻译阶段扮演了验证步骤的角色。因此,它成为获取专家难以形式化的知识的一种非常有效的方法。使用一阶逻辑确实会使事情复杂化,但作为奖励,它提供了一种强大的方法来提取和验证所获得的知识,特别是当领域专家无法用简单的方式表达他的知识时。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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