Wichlacz, Julia; Höller, Daniel; Torralba, Álvaro; Hoffmann, Jörg
Applying Monte-Carlo Tree Search in HTN Planning
Proceedings of the 13th International Symposium on Combinatorial Search (SoCS), AAAI Press, pp. 82-90, Vienna, Austria, 2020.
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.