Bridging Engineering and AI Planning through Model-Based Knowledge Transformation for the Validation of Automated Production System Variants
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918141290872832 |
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| author | Nabizada, Hamied Beers, Lasse Chahine, Alain Gehlhoff, Felix Niggemann, Oliver Fay, Alexander |
| author_facet | Nabizada, Hamied Beers, Lasse Chahine, Alain Gehlhoff, Felix Niggemann, Oliver Fay, Alexander |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12091 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridging Engineering and AI Planning through Model-Based Knowledge Transformation for the Validation of Automated Production System Variants Nabizada, Hamied Beers, Lasse Chahine, Alain Gehlhoff, Felix Niggemann, Oliver Fay, Alexander Artificial Intelligence 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. |
| title | Bridging Engineering and AI Planning through Model-Based Knowledge Transformation for the Validation of Automated Production System Variants |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.12091 |