# Automatically Learning HTN Methods from Landmarks

arXiv: https://arxiv.org/abs/2404.06325 · HTML: https://mlsanity.com/paper/2404.06325/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 29 references that are in the map.
- First posted on arXiv 9 Apr 2024.

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

- [The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks](https://mlsanity.com/paper/1401.3839/) (2014)

## 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 Network (HTN) planning usually requires a domain engineer to provide manual input about how to decompose a planning problem. Even HTN-MAKER, a well-known method-learning algorithm, requires a domain engineer to annotate the tasks with information about what to learn. We introduce CURRICULAMA, an HTN method learning algorithm that completely automates the learning process. It uses landmark analysis to compose annotated tasks and leverages curriculum learning to order the learning of methods from simpler to more complex. This eliminates the need for manual input, resolving a core issue with HTN-MAKER. We prove CURRICULAMA's soundness, and show experimentally that it has a substantially similar convergence rate in learning a complete set of methods to HTN-MAKER.