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. 
Sandra Castellanos-Paez (LIG Laboratoire d'Informatique de Grenoble),   Damien Pellier (LIG Laboratoire d'Informatique de Gr...
cs.AI