  We consider an original problem that arises from the issue of security
analysis of a power system and that we name optimal discovery with
probabilistic expert advice. We address it with an algorithm based on the
optimistic paradigm and the Good-Turing missing mass estimator. We show that
this strategy uniformly attains the optimal discovery rate in a macroscopic
limit sense, under some assumptions on the probabilistic experts. We also
provide numerical experiments suggesting that this optimal behavior may still
hold under weaker assumptions.
