  We apply the method of defensive forecasting, based on the use of
game-theoretic supermartingales, to prediction with expert advice. In the
traditional setting of a countable number of experts and a finite number of
outcomes, the Defensive Forecasting Algorithm is very close to the well-known
Aggregating Algorithm. Not only the performance guarantees but also the
predictions are the same for these two methods of fundamentally different
nature. We discuss also a new setting where the experts can give advice
conditional on the learner's future decision. Both the algorithms can be
adapted to the new setting and give the same performance guarantees as in the
traditional setting. Finally, we outline an application of defensive
forecasting to a setting with several loss functions.
