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

2025-09-15

AI Summary

Engineering models (e.g., SysML) lack formal planning semantics—such as preconditions, effects, resource constraints, and temporal bounds—hindering task reachability and performance evaluation across system variants. Method: This paper proposes a model-driven approach natively integrated into SysML, leveraging a custom SysML profile to embed symbolic planning semantics directly into engineering models. It enables fully automated, bidirectional transformation from SysML models to PDDL domain and problem files—without external models or manual intervention—ensuring semantic consistency and model reusability. The method synergistically combines model transformation algorithms with symbolic planning techniques. Contribution/Results: Evaluated on an aircraft assembly case study, the approach validates functional feasibility and execution efficiency across multiple system variants. It significantly enhances interoperability between Model-Based Systems Engineering (MBSE) and AI planning, advancing automation and rigor in early-phase system design and analysis.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Multiagent Systems: Multiagent Planning
• Cognitive Modeling & Cognitive Systems: Symbolic Representations

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

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.

Problem

• Bridging engineering models and AI planning semantics
• Automating generation of symbolic planning artifacts from SysML
• Validating automated production system variants through AI planning

Innovation

• Model-driven generation of symbolic planning artifacts
• SysML profile integration with reusable stereotypes
• Automated PDDL domain and problem file creation