  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 two
different regret bounds 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. Finally, we provide numerical
experiments illustrating these theoretical findings.
