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.
@article{arxiv.1810.09145,
title = {Mining useful Macro-actions in Planning},
author = {Sandra Castellanos-Paez and Damien Pellier and Humbert Fiorino and Sylvie Pesty},
journal= {arXiv preprint arXiv:1810.09145},
year = {2018}
}
Comments
International Conference on Artificial Intelligence and Pattern Recognition, 2016