# MyPDDL: Tools for efficiently creating PDDL domains and problems

arXiv: https://arxiv.org/abs/2008.11069 · HTML: https://mlsanity.com/paper/2008.11069/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2020.

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

- [Integrating AI Planning Semantics into SysML System Models for Automated PDDL File Generation](https://mlsanity.com/paper/2506.06714/) (2025)
- [Bridging Engineering and AI Planning through Model-Based Knowledge Transformation for the Validation of Automated Production System Variants](https://mlsanity.com/paper/2509.12091/) (2025)
- [APTree: Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Construction](https://mlsanity.com/paper/2610.18523/) (2026)

## Abstract (arXiv)

The Planning Domain Definition Language (PDDL) is the state-of-the-art language for specifying planning problems in artificial intelligence research. Writing and maintaining these planning problems, however, can be time-consuming and error prone. To address this issue, we present myPDDL-a modular toolkit for developing and manipulating PDDL domains and problems. To evaluate myPDDL, we compare its features to existing knowledge engineering tools for PDDL. In a user test, we additionally assess two of its modules, namely the syntax highlighting feature and the type diagram generator. The users of syntax highlighting detected 36% more errors than non-users in an erroneous domain file. The average time on task for questions on a PDDL type hierarchy was reduced by 48% when making the type diagram generator available. This implies that myPDDL can support knowledge engineers well in the PDDL design and analysis process.