Mining useful Macro-actions in Planning
This work addresses the challenge of efficient planning for AI systems, but it is incremental as it builds on existing macro-action approaches.
The authors tackled the problem of scaling up plan synthesis by proposing an algorithm to identify useful macro-actions using data mining techniques, resulting in significant improvements over six classical planning benchmarks.
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 six classical planning benchmarks.