@inproceedings{9513ec5c85784954aa4ebbb776cc3e72,
title = "Applying monte-carlo tree search in HTN planning",
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.",
author = "Julia Wichlacz and Daniel H{\"o}ller and {\'A}lvaro Torralba and J{\"o}rg Hoffmann",
year = "2020",
language = "English",
series = "Proceedings of the 13th International Symposium on Combinatorial Search, SoCS 2020",
pages = "82--90",
editor = "Daniel Harabor and Mauro Vallati",
booktitle = "Proceedings of the 13th International Symposium on Combinatorial Search, SoCS 2020",
publisher = "The AAAI Press",
note = "13th International Symposium on Combinatorial Search, SoCS 2020 ; Conference date: 26-05-2020 Through 28-05-2020",
}