Mining useful Macro-actions in Planning

Abstract--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 online macro-actions, we propose an algorithm to identify useful macroactions based on data mining techniques. The integration in the planning search of these learned macro-actions shows significant improvements over six classical planning benchmarks. Automated planning is an area of Artificial Intelligence that comes up with the challenge of devising systems that can autonomously find a plan to reach a set of goals. In classical planning, a problem is composed of an initial state, a goal specification and a set of actions. From the initial state if the preconditions of an action are satisfied, the action is applicable to the current state.