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Model-based reasoning for system and software engineering: The Knowledge From Pictures (KFP) environment

机译:系统和软件工程的基于模型的推理:图片知识(KFP)环境

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摘要

This paper presents a discussion of current work in the area of graphical modeling and model-based reasoning being undertaken by the Automation Technology Section, Code 522.3, at Goddard. The work was initially motivated by the growing realization that the knowledge acquisition process was a major bottleneck in the generation of fault detection, isolation, and repair (FDIR) systems for application in automated Mission Operations. As with most research activities this work started out with a simple objective: to develop a proof-of-concept system demonstrating that a draft rule-base for a FDIR system could be automatically realized by reasoning from a graphical representation of the system to be monitored. This work was called Knowledge From Pictures (KFP) (Truszkowski et. al. 1992). As the work has successfully progressed the KFP tool has become an environment populated by a set of tools that support a more comprehensive approach to model-based reasoning. This paper continues by giving an overview of the graphical modeling objectives of the work, describing the three tools that now populate the KFP environment, briefly presenting a discussion of related work in the field, and by indicating future directions for the KFP environment.
机译:本文介绍了戈达德代码522.3的自动化技术部在图形建模和基于模型的推理领域中的最新工作。这项工作最初是由于人们日益认识到,知识获取过程是故障诊断,隔离和修复(FDIR)系统的生成中的主要瓶颈,该系统可应用于自动任务行动。与大多数研究活动一样,这项工作从一个简单的目标开始:开发概念验证系统,该系统证明可以通过从要监视的系统的图形表示中进行推理来自动实现FDIR系统的规则库草案。 。这项工作被称为图片知识(KFP)(Truszkowski等,1992)。随着工作的顺利进行,KFP工具已成为由一组工具组成的环境,这些工具支持更全面的基于模型的推理方法。本文通过概述工作的图形建模目标,描述现在构成KFP环境的三个工具,简要介绍该领域的相关工作以及指明KFP环境的未来发展方向继续进行。

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