Translating HTNs to PDDL: A Small Amount of Domain Knowledge Can Go a Long Way
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 1629-1633.
Abstract
We show how to translate HTN domain descriptions (if they satisfy certain restrictions) into PDDL so that they can be used by classical planners. We provide correctness results for our translation algorithm, and show that it runs in linear time and space. We also show that even small and incomplete amounts of HTN knowledge, when translated into PDDL using our algorithm, can greatly improve a classical planner's performance. In experiments on several thousand randomly generated problems in three different planning domains, such knowledge speeded up the well-known Fast-Forward planner by several orders of magnitude, and enabled it to solve much larger problems than it could otherwise solve.Ronald Wayne Alford, Ugur Kuter, Dana Nau
@inproceedings{ijcai2009p272,
title = {Translating HTNs to PDDL: A Small Amount of Domain Knowledge Can Go a Long Way},
author = {Alford, Ronald and Kuter, Ugur and Nau, Dana S.},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {1629--1633},
year = {2009},
url = {https://ijcai.org/proceedings/2009/272},
}
TY - CPAPER TI - Translating HTNs to PDDL: A Small Amount of Domain Knowledge Can Go a Long Way AU - Alford, Ronald AU - Kuter, Ugur AU - Nau, Dana S. T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 1629 EP - 1633 UR - https://ijcai.org/proceedings/2009/272 ER -Copied
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