  Scenario discovery is the process of finding areas of interest, known as
scenarios, in data spaces resulting from simulations. For instance, one might
search for conditions, i.e., inputs of the simulation model, where the system
is unstable. Subgroup discovery methods are commonly used for scenario
discovery. They find scenarios in the form of hyperboxes, which are easy to
comprehend. Given a computational budget, results tend to get worse as the
number of inputs of the simulation model and the cost of simulations increase.
We propose a new procedure for scenario discovery from few simulations, dubbed
REDS. A key ingredient is using an intermediate machine learning model to label
data for subsequent use by conventional subgroup discovery methods. We provide
statistical arguments why this is an improvement. In our experiments, REDS
reduces the number of simulations required by 50--75\% on average, depending on
the quality measure. It is also useful as a semi-supervised subgroup discovery
method and for discovering better scenarios from third-party data, when a
simulation model is not available.
