# Bridging Engineering and AI Planning through Model-Based Knowledge Transformation for the Validation of Automated Production System Variants

arXiv: https://arxiv.org/abs/2509.12091 · HTML: https://mlsanity.com/paper/2509.12091/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Builds on (most cited first, 6 of 6)

- [LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models](https://mlsanity.com/paper/2503.18971/) (2025)
- [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)
- [Integrating AI Planning Semantics into SysML System Models for Automated PDDL File Generation](https://mlsanity.com/paper/2506.06714/) (2025)

## Built on by (most cited first, 1 of 1)

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

## Abstract (arXiv)

Engineering models created in Model-Based Systems Engineering (MBSE) environments contain detailed information about system structure and behavior. However, they typically lack symbolic planning semantics such as preconditions, effects, and constraints related to resource availability and timing. This limits their ability to evaluate whether a given system variant can fulfill specific tasks and how efficiently it performs compared to alternatives. To address this gap, this paper presents a model-driven method that enables the specification and automated generation of symbolic planning artifacts within SysML-based engineering models. A dedicated SysML profile introduces reusable stereotypes for core planning constructs. These are integrated into existing model structures and processed by an algorithm that generates a valid domain file and a corresponding problem file in Planning Domain Definition Language (PDDL). In contrast to previous approaches that rely on manual transformations or external capability models, the method supports native integration and maintains consistency between engineering and planning artifacts. The applicability of the method is demonstrated through a case study from aircraft assembly. The example illustrates how existing engineering models are enriched with planning semantics and how the proposed workflow is applied to generate consistent planning artifacts from these models. The generated planning artifacts enable the validation of system variants through AI planning.