In this paper, we address the problem of finding the part of the observations that is useful for the diagnosis. We define a as an abstraction of the observations. We then argue that a sub-observation is if it allows a diagnoser to derive the same minimal diagnosis as the original observations; and we define as a maximally abstracted sufficient sub-observation. We show how to compute a critical observation, and discuss a number of algorithmic improvements that also shed light on the theory of critical observations. Finally, we illustrate this framework on both state-based and event-based observations.

中文翻译：

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基于模型的诊断的关键观察

在本文中，我们解决了寻找对诊断有用的观察部分的问题。我们将 a 定义为观察结果的抽象。然后我们认为，子观察是否允许诊断者得出与原始观察相同的最小诊断；我们将其定义为最大程度抽象的充分子观察。我们展示了如何计算关键观察，并讨论了一些算法改进，这些改进也阐明了关键观察理论。最后，我们通过基于状态和基于事件的观察来说明这个框架。