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In many model-based diagnosis applications it is impossible to provide such aset of observations and/or measurements that allow to identify the real causeof a fault. Therefore, diagnosis systems often return many possible candidates,leaving the burden of selecting the correct diagnosis to a user. Sequentialdiagnosis techniques solve this problem by automatically generating a sequenceof queries to some oracle. The answers to these queries provide additionalinformation necessary to gradually restrict the search space by removingdiagnosis candidates inconsistent with the answers.  During query computation, existing sequential diagnosis methods often requirethe generation of many unnecessary query candidates and strongly rely onexpensive logical reasoners. We tackle this issue by devising efficientheuristic query search methods. The proposed methods enable for the first timea completely reasoner-free query generation while at the same time guaranteeingoptimality conditions, e.g. minimal cardinality or best understandability, ofthe returned query that existing methods cannot realize. Hence, the performanceof this approach is independent of the (complexity of the) diagnosed system.Experiments conducted using real-world problems show that the new approach ishighly scalable and outperforms existing methods by orders of magnitude.

Scalable Computation of Optimized Queries for Sequential Diagnosis