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
on
the Good-Turing missing mass estimator. We prove a regret bound on the
performance of this algorithm under weak assumptions on the probabilistic
experts. Under more restrictive hypotheses, we also prove a macroscopic
optimality result, comparing the algorithm both with  an oracle strategy and
with uniform sampling.