AAAI Publications, Thirteenth Annual Symposium on Combinatorial Search

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Applying Monte-Carlo Tree Search in HTN Planning
Julia Wichlacz, Daniel Höller, Alvaro Torralba, Jörg Hoffmann

Last modified: 2020-05-08

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.


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