@inproceedings{Magnaguagno2017,
author = {Maur\'{i}cio C. Magnaguagno and Felipe Meneguzzi},
title = {{Method Composition through Operator Pattern Identification}},
booktitle = {Proceedings of the 2017 Workshop on Knowledge Engineering for Planning and Scheduling (KEPS@ICAPS)},
year = {2017},
publisher = {AAAI Press},
abstract = {Classical planning is a computationally expensive task, especially when tackling real world problems. To overcome such limitations, most realistic applications of planning rely on domain knowledge configured by a domain expert, such as the hierarchy of tasks and methods used by Hierarchical Task Network (HTN) planning. Thus, the efficiency of HTN approaches relies heavily on human-driven domain design.
In this paper, we aim to address this limitation by developing an approach to generate useful methods based on classical domains. Our work does not require annotations in the classical planning operators or training examples, and instead, relies solely on operator descriptions to identify task patterns and the sub-problems related to each pattern. We propose the use of methods that solve common sub-problems to obtain HTN methods automatically.}
}