DynamicHS：精简Reiter的击中集树用于顺序诊断
DynamicHS: Streamlining Reiter's Hitting-Set Tree for Sequential Diagnosis
摘要：
给定一个不按预期工作的系统，序贯诊断（SD）旨在建议一系列系统测量，以将系统错误行为的真实解释与可能的解释的潜在指数集隔离开来。为了推理下一个最佳测量，SD方法通常需要在迭代诊断过程的每一步都有可能的故障解释样本。该样本的计算可以通过各种诊断搜索算法来完成。其中，Reiter的HS树是最受欢迎的一个，由于其理想的属性和普遍适用性。通常，HS树在整个SD过程中以无状态的方式使用，以在每次迭代中（重新）计算可能的故障解释的样本，每次都给出最新（更新）的系统知识，包括迄今为止收集的所有测量值。在这一点上，构建的搜索树在两次迭代之间被丢弃，尽管通常树的大部分必须在下一次迭代中重建，这涉及冗余操作和对昂贵的推理服务的调用。作为一种补救措施，我们提出DynamicHS，HS树的一个变种，在整个诊断会话中保持状态，并另外采用特殊的策略，以尽量减少昂贵的推理机调用的数量。在这种情况下，DynamicHS为Raymond Reiter在1987年的开创性论文中提出的一个长期问题提供了答案。通过对实际诊断问题的大量评估，证明了DynamicHS的合理性，并证明了其明显优于HS-Tree。计算时间更具体地说，DynamicHS在96%的执行顺序诊断会话中优于HS-Tree，并且每次运行，后者所需的时间高达前者的800%。值得注意的是，DynamicHS实现了这些性能改进，同时保留了所有理想的属性以及HS-Tree的一般适用性。
英文摘要：
Given a system that does not work as expected, Sequential Diagnosis (SD) aims at suggesting a series of system measurements to isolate the true explanation for the system's misbehavior from a potentially exponential set of possible explanations. To reason about the best next measurement, SD methods usually require a sample of possible fault explanations at each step of the iterative diagnostic process. The computation of this sample can be accomplished by various diagnostic search algorithms. Among those, Reiter's HS-Tree is one of the most popular due its desirable properties and general applicability. Usually, HS-Tree is used in a stateless fashion throughout the SD process to (re)compute a sample of possible fault explanations in each iteration, each time given the latest (updated) system knowledge including all so-far collected measurements. At this, the built search tree is discarded between two iterations, although often large parts of the tree have to be rebuilt in the next iteration, involving redundant operations and calls to costly reasoning services. As a remedy to this, we propose DynamicHS, a variant of HS-Tree that maintains state throughout the diagnostic session and additionally embraces special strategies to minimize the number of expensive reasoner invocations. In this vein, DynamicHS provides an answer to a longstanding question posed by Raymond Reiter in his seminal paper from 1987. Extensive evaluations on real-world diagnosis problems prove the reasonability of the DynamicHS and testify its clear superiority to HS-Tree wrt. computation time. More specifically, DynamicHS outperformed HS-Tree in 96% of the executed sequential diagnosis sessions and, per run, the latter required up to 800% the time of the former. Remarkably, DynamicHS achieves these performance improvements while preserving all desirable properties as well as the general applicability of HS-Tree.
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