Model-based Workflow for the Automated Generation of PDDL Descriptions
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Abstract (arXiv)
Manually creating Planning Domain Definition Language (PDDL) descriptions is difficult, error-prone, and requires extensive expert knowledge. However, this knowledge is already embedded in engineering models and can be reused. Therefore, this contribution presents a comprehensive workflow for the automated generation of PDDL descriptions from integrated system and product models. The proposed workflow leverages Model-Based Systems Engineering (MBSE) to organize and manage system and product information, translating it automatically into PDDL syntax for planning purposes. By connecting system and product models with planning aspects, it ensures that changes in these models are quickly reflected in updated PDDL descriptions, facilitating efficient and adaptable planning processes. The workflow is validated within a use case from aircraft assembly.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 1 of its 19 references that are in the map. First posted on arXiv 15 Aug 2024.
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1 of 19 references.
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Citing papers per year: 2025: 3; 2026: 1.
4 papers.
- 2025LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Modelscited by 38
- 2025Integrating AI Planning Semantics into SysML System Models for Automated PDDL File Generationcited by 2
- 2025Bridging Engineering and AI Planning through Model-Based Knowledge Transformation for the Validation of Automated Production System Variantscited by 1
- 2026From Capability Models to Automated Planning: An AAS-Native Approach for Automatic PDDL Generationcited by 0
- 2026From Capability Models to Automated Planning: An AAS-Native Approach for Automatic PDDL Generation1 Jun 2026
- 2025Bridging Engineering and AI Planning through Model-Based Knowledge Transformation for the Validation of Automated Production System Variants15 Sep 2025
- 2025Integrating AI Planning Semantics into SysML System Models for Automated PDDL File Generation7 Jun 2025
- 2025LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models22 Mar 2025