Learning Macro-actions for State-Space Planning
  Planning has achieved significant progress in recent years. Among the various
approaches to scale up plan synthesis, the use of macro-actions has been widely
explored. As a first stage towards the development of a solution to learn
on-line macro-actions, we propose an algorithm to identify useful macro-actions
based on data mining techniques. The integration in the planning search of
these learned macro-actions shows significant improvements over four classical
planning benchmarks.
