# HyperTensioN and Total-order Forward Decomposition optimizations

arXiv: https://arxiv.org/abs/2207.00345 · HTML: https://mlsanity.com/paper/2207.00345/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 38 references that are in the map.
- First posted on arXiv 1 Jul 2022.

## Builds on (most cited first, 1 of 1)

- [From Classical to Hierarchical: benchmarks for the HTN Track of the International Planning Competition](https://mlsanity.com/paper/2103.05481/) (2021)

## Built on by (most cited first, 1 of 1)

- [Hierarchical Task Network Planning with LLM-Generated Heuristics](https://mlsanity.com/paper/2605.07707/) (2026)

## Abstract (arXiv)

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.