Scalable Computation of Optimized Queries for Sequential Diagnosis
  In many model-based diagnosis applications it is impossible to provide such a
set of observations and/or measurements that allow to identify the real cause
of a fault. Therefore, diagnosis systems often return many possible candidates,
leaving the burden of selecting the correct diagnosis to a user. Sequential
diagnosis techniques solve this problem by automatically generating a sequence
of queries to some oracle. The answers to these queries provide additional
information necessary to gradually restrict the search space by removing
diagnosis candidates inconsistent with the answers.
  During query computation, existing sequential diagnosis methods often require
the generation of many unnecessary query candidates and strongly rely on
expensive logical reasoners. We tackle this issue by devising efficient
heuristic query search methods. The proposed methods enable for the first time
a completely reasoner-free query generation while at the same time guaranteeing
optimality conditions, e.g. minimal cardinality or best understandability, of
the returned query that existing methods cannot realize. Hence, the performance
of this approach is independent of the (complexity of the) diagnosed system.
Experiments conducted using real-world problems show that the new approach is
highly scalable and outperforms existing methods by orders of magnitude.
