  We have recently introduced a system that automatically generates robotic
planning operators from human demonstrations. One feature of our system is the
operator count, which keeps track of the application frequency of every
operator within the demonstrations. In this extended abstract, we show that we
can use the count to slim down domains with the goal of decreasing the search
time for long-horizon planning goals. The conceptual idea behind our approach
is that we would like to prioritize operators that have occurred more often in
the demonstrations over those that were not observed so frequently. We,
therefore, propose to limit the domain only to the most popular operators. If
this subset of operators is not sufficient to find a plan, we iteratively
expand this subset of operators. We show that this significantly reduces the
search time for long-horizon planning goals.
