  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.
