Evaluating Active Learning Heuristics for Sequential Diagnosis
  Given a malfunctioning system, sequential diagnosis aims at identifying the
root cause of the failure in terms of abnormally behaving system components. As
initial system observations usually do not suffice to deterministically pin
down just one explanation of the system's misbehavior, additional system
measurements can help to differentiate between possible explanations. The goal
is to restrict the space of explanations until there is only one (highly
probable) explanation left. To achieve this with a minimal-cost set of
measurements, various (active learning) heuristics for selecting the best next
measurement have been proposed.
  We report preliminary results of extensive ongoing experiments with a set of
selection heuristics on real-world diagnosis cases. In particular, we try to
answer questions such as "Is some heuristic always superior to all others?",
"On which factors does the (relative) performance of the particular heuristics
depend?" or "Under which circumstances should I use which heuristic?"
