HyperTensioN and Total-order Forward Decomposition optimizations
Artificial Intelligence
2022-07-04 v1 Multiagent Systems
Systems and Control
Systems and Control
Abstract
Hierarchical Task Networks (HTN) planners generate plans using a decomposition process with extra domain knowledge to guide search towards a planning task. While domain experts develop HTN descriptions, they may repeatedly describe the same preconditions, or methods that are rarely used or possible to be decomposed. By leveraging a three-stage compiler design we can easily support more language descriptions and preprocessing optimizations that when chained can greatly improve runtime efficiency in such domains. In this paper we evaluate such optimizations with the HyperTensioN HTN planner, used in the HTN IPC 2020.
Cite
@article{arxiv.2207.00345,
title = {HyperTensioN and Total-order Forward Decomposition optimizations},
author = {Maurício Cecílio Magnaguagno and Felipe Meneguzzi and Lavindra de Silva},
journal= {arXiv preprint arXiv:2207.00345},
year = {2022}
}
Comments
Preprint version of journal submission