论文标题
声音，完整，线性空间，最佳诊断搜索
Sound, Complete, Linear-Space, Best-First Diagnosis Search
论文作者
论文摘要
各种基于模型的诊断方案需要计算最优选的故障解释。但是，现有的算法是声音（即仅输出实际故障说明）并完成（即可以返回所有说明），但是，需要指数空间才能完成此任务。作为一种补救措施，为了在内存限制的设备上成功诊断和记忆密集型问题案例，我们提出了RBF-HS，这是一种基于Korf众所周知的RBFS算法的诊断搜索方法。 RBF-HS可以在线性空间范围内以最佳优势列出任意固定数量的故障解释，而不会牺牲理想的声音或完整性属性。使用现实世界中诊断病例的评估表明，RBF-HS在计算最小信号故障的解释时，在大多数情况下，可以节省大量空间（最高98％），而仅需要比Reiter的HS-Tree（通常使用的HS-Tree），通常使用的是一种常用，并且通常适用于完整的声音，完整且最佳的诊断。
Various model-based diagnosis scenarios require the computation of the most preferred fault explanations. Existing algorithms that are sound (i.e., output only actual fault explanations) and complete (i.e., can return all explanations), however, require exponential space to achieve this task. As a remedy, to enable successful diagnosis on memory-restricted devices and for memory-intensive problem cases, we propose RBF-HS, a diagnostic search method based on Korf's well-known RBFS algorithm. RBF-HS can enumerate an arbitrary fixed number of fault explanations in best-first order within linear space bounds, without sacrificing the desirable soundness or completeness properties. Evaluations using real-world diagnosis cases show that RBF-HS, when used to compute minimum-cardinality fault explanations, in most cases saves substantial space (up to 98 %) while requiring only reasonably more or even less time than Reiter's HS-Tree, a commonly used and as generally applicable sound, complete and best-first diagnosis search.
