Learning Macro-actions for State-Space Planning
Artificial Intelligence
2016-10-10 v1
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 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.
Cite
@article{arxiv.1610.02293,
title = {Learning Macro-actions for State-Space Planning},
author = {Sandra Castellanos-Paez and Damien Pellier and Humbert Fiorino and Sylvie Pesty},
journal= {arXiv preprint arXiv:1610.02293},
year = {2016}
}
Comments
Journ{\'e}es Francophones sur la Planification, la D{\'e}cision et l'Apprentissage pour la conduite de syst{\`e}mes (JFPDA 2016) , Jul 2016, Grenoble, France. 2016