Many domains require scalable algorithms that help to determine diagnoses efficiently and often within predefined time limits. Anytime diagnosis is able to determine solutions in such a way and thus is especially useful in real-time scenarios such as production scheduling, robot control, and communication networks management where diagnosis and corresponding reconfiguration capabilities play a major role. Anytime diagnosis in many cases comes along with a trade-off between diagnosis quality and the efficiency of diagnostic reasoning. In this paper we introduce and analyze FlexDiag which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain. Results show that FlexDiag helps to significantly increase the performance of direct diagnosis search with corresponding quality tradeoffs in terms of minimality and accuracy.

中文翻译：

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随时诊断重新配置

许多域都需要可伸缩的算法，这些算法有助于在通常的预定义时间限制内有效地确定诊断。任何时候诊断都能够以这种方式确定解决方案，因此在诊断和相应的重新配置功能起主要作用的生产计划，机器人控制和通信网络管理等实时场景中特别有用。在许多情况下，随时进行诊断都需要在诊断质量和诊断推理效率之间进行权衡。在本文中，我们介绍和分析FlexDiag，这是一种随时可直接诊断的方法。我们使用来自功能模型领域的配置基准和来自汽车领域的工业配置知识库，就性能和诊断质量评估算法。