# PDDLFuse: A Tool for Generating Diverse Planning Domains

arXiv: https://arxiv.org/abs/2411.19886 · HTML: https://mlsanity.com/paper/2411.19886/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 29 Nov 2024.

## Builds on (most cited first, 7 of 7)

- [Active Domain Randomization](https://mlsanity.com/paper/1904.04762/) (2019)
- [A Short Tutorial on The Weisfeiler-Lehman Test And Its Variants](https://mlsanity.com/paper/2201.07083/) (2022)
- [Multimodal Procedural Planning via Dual Text-Image Prompting](https://mlsanity.com/paper/2305.01795/) (2023)
- [Action Schema Networks: Generalised Policies with Deep Learning](https://mlsanity.com/paper/1709.04271/) (2017)
- [Learning Domain-Independent Heuristics for Grounded and Lifted Planning](https://mlsanity.com/paper/2312.11143/) (2023)
- [Automating the Generation of Prompts for LLM-based Action Choice in PDDL Planning](https://mlsanity.com/paper/2311.09830/) (2023)
- [Towards Learning Foundation Models for Heuristic Functions to Solve Pathfinding Problems](https://mlsanity.com/paper/2406.02598/) (2024)

## Built on by (most cited first, 3 of 3)

- [LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models](https://mlsanity.com/paper/2503.18971/) (2025)
- [Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM](https://mlsanity.com/paper/2505.08492/) (2025)
- [Reasoning Core: A Scalable Procedural Data Generation Suite for Symbolic Pre-training and Post-Training](https://mlsanity.com/paper/2603.02208/) (2026)

## Abstract (arXiv)

Various real-world challenges require planning algorithms that can adapt to a broad range of domains. Traditionally, the creation of planning domains has relied heavily on human implementation, which limits the scale and diversity of available domains. While recent advancements have leveraged generative AI technologies such as large language models (LLMs) for domain creation, these efforts have predominantly focused on translating existing domains from natural language descriptions rather than generating novel ones. In contrast, the concept of domain randomization, which has been highly effective in reinforcement learning, enhances performance and generalizability by training on a diverse array of randomized new domains. Inspired by this success, our tool, PDDLFuse, aims to bridge this gap in Planning Domain Definition Language (PDDL). PDDLFuse is designed to generate new, diverse planning domains that can be used to validate new planners or test foundational planning models. We have developed methods to adjust the domain generators parameters to modulate the difficulty of the domains it generates. This adaptability is crucial as existing domain-independent planners often struggle with more complex problems. Initial tests indicate that PDDLFuse efficiently creates intricate and varied domains, representing a significant advancement over traditional domain generation methods and making a contribution towards planning research.