@inproceedings{Wichlacz2020SoCS,
title     = {Applying Monte-Carlo Tree Search in HTN Planning},
author    = {Julia Wichlacz and Daniel H\"{o}ller and \'{A}lvaro Torralba and J\"{o}rg Hoffmann},
year      = {2020},
pages     = {82--90},
publisher = {AAAI Press},
booktitle = {Proceedings of the 13th International Symposium on Combinatorial Search (SoCS)},
abstract  = {Search methods are useful in hierarchical task network (HTN) planning to make performance less dependent on the domain knowledge provided, and to minimize plan costs. Here we investigate Monte-Carlo tree search (MCTS) as a new algorithmic alternative in HTN planning. We implement combinations of MCTS with heuristic search in Panda. We furthermore investigate MCTS in JSHOP, to address lifted (non-grounded) planning, leveraging the fact that, in contrast to other search methods, MCTS does not require a grounded task representation. Our new methods yield coverage performance on par with the state of the art, but in addition can effectively minimize plan cost over time.}
}