# An Accurate HDDL Domain Learning Algorithm from Partial and Noisy Observations

arXiv: https://arxiv.org/abs/2206.06882 · HTML: https://mlsanity.com/paper/2206.06882/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 49 references that are in the map.
- First posted on arXiv 14 Jun 2022.

## Builds on (most cited first, 3 of 3)

- [Hierarchical Planning in the IPC](https://mlsanity.com/paper/1909.04405/) (2019)
- [AMLSI: A Novel Accurate Action Model Learning Algorithm](https://mlsanity.com/paper/2011.13277/) (2020)
- [Totally and Partially Ordered Hierarchical Planners in PDDL4J Library](https://mlsanity.com/paper/2011.13297/) (2020)

## Built on by (most cited first, 2 of 2)

- [HDDLGym: A Tool for Studying Multi-Agent Hierarchical Problems Defined in HDDL with OpenAI Gym](https://mlsanity.com/paper/2505.22597/) (2025)
- [Hierarchical Task Network Planning with LLM-Generated Heuristics](https://mlsanity.com/paper/2605.07707/) (2026)

## Abstract (arXiv)

The Hierarchical Task Network (sf HTN) formalism is very expressive and used to express a wide variety of planning problems. In contrast to the classical sf STRIPS formalism in which only the action model needs to be specified, the sf HTN formalism requires to specify, in addition, the tasks of the problem and their decomposition into subtasks, called sf HTN methods. For this reason, hand-encoding sf HTN problems is considered more difficult and more error-prone by experts than classical planning problem. To tackle this problem, we propose a new approach (HierAMLSI) based on grammar induction to acquire sf HTN planning domain knowledge, by learning action models and sf HTN methods with their preconditions. Unlike other approaches, HierAMLSI is able to learn both actions and methods with noisy and partial inputs observation with a high level or accuracy.