# Integrating AI Planning Semantics into SysML System Models for Automated PDDL File Generation

arXiv: https://arxiv.org/abs/2506.06714 · HTML: https://mlsanity.com/paper/2506.06714/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 40 references that are in the map.
- First posted on arXiv 7 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [A Research Agenda for AI Planning in the Field of Flexible Production Systems](https://mlsanity.com/paper/2112.15484/) (2021)
- [Flexible Production Systems: Automated Generation of Operations Plans Based on ISA-95 and PDDL](https://mlsanity.com/paper/1911.05481/) (2019)
- [Model-based Workflow for the Automated Generation of PDDL Descriptions](https://mlsanity.com/paper/2408.08145/) (2024)
- [MyPDDL: Tools for efficiently creating PDDL domains and problems](https://mlsanity.com/paper/2008.11069/) (2020)
- [A SysML Profile for the Standardized Description of Processes during System Development](https://mlsanity.com/paper/2403.06723/) (2024)

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

- [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)
- [From Capability Models to Automated Planning: An AAS-Native Approach for Automatic PDDL Generation](https://mlsanity.com/paper/2606.02167/) (2026)

## Abstract (arXiv)

This paper presents a SysML profile that enables the direct integration of planning semantics based on the Planning Domain Definition Language (PDDL) into system models. Reusable stereotypes are defined for key PDDL concepts such as types, predicates, functions and actions, while formal OCL constraints ensure syntactic consistency. The profile was derived from the Backus-Naur Form (BNF) definition of PDDL 3.1 to align with SysML modeling practices. A case study from aircraft manufacturing demonstrates the application of the profile: a robotic system with interchangeable end effectors is modeled and enriched to generate both domain and problem descriptions in PDDL format. These are used as input to a PDDL solver to derive optimized execution plans. The approach supports automated and model-based generation of planning descriptions and provides a reusable bridge between system modeling and AI planning in engineering design.