Learning Macro-actions for State-Space Planning

Sandra Castellanos-Paez (1) , Damien Pellier (1) , Humbert Fiorino (1) , Sylvie Pesty (1)

1 MAGMA - Modélisation d’agents autonomes en univers multi-agents

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.

Mots clés: Automated Planning; Data Mining; Macro-actions; Learning

Domaines: Intelligence artificielle [cs.AI]

Sandra Castellanos-Paez, Damien Pellier, Humbert Fiorino, Sylvie Pesty. Learning Macro-actions for State-Space Planning. Journées Francophones sur la Planification, la Décision et l'Apprentissage, Jul 2016, Grenoble, France. ⟨hal-01365366⟩