Inexpensive Cost-Optimized Measurement Proposal for Sequential
  Model-Based Diagnosis
  In this work we present strategies for (optimal) measurement selection in
model-based sequential diagnosis. In particular, assuming a set of leading
diagnoses being given, we show how queries (sets of measurements) can be
computed and optimized along two dimensions: expected number of queries and
cost per query. By means of a suitable decoupling of two optimizations and a
clever search space reduction the computations are done without any inference
engine calls. For the full search space, we give a method requiring only a
polynomial number of inferences and guaranteeing query properties existing
methods cannot provide. Evaluation results using real-world problems indicate
that the new method computes (virtually) optimal queries instantly
independently of the size and complexity of the considered diagnosis problems.
