Integrating Machine Learning into an SMT-based Planning Approach for Production Planning in Cyber-Physical Production Systems
Abstract
Cyber-Physical Production Systems (CPPS) are highly complex systems, making the application of AI planning approaches for production planning challenging. Most AI planning approaches require comprehensive domain descriptions which model the functional dependencies within the CPPS. Though, due to their high complexity, manually creating those domain descriptions is difficult, tedious, and error-prone. We therefore propose a novel generic planning approach that is able to integrate mathematical formulas or Machine Learning models into a symbolic SMT-based planning algorithm, shedding the need for complex manually created models. Our approach uses a feature-vector-based state-space representation as an interface of symbolic and sub-symbolic AI, and can identify a solution to CPPS planning problems by determining the required production steps, their sequence, and their parametrization. We evaluate our approach on twelve planning problems from a real CPPS, demonstrating its ability to express complex dependencies within production steps as mathematical formulas or integrating ML models. The code is publicly available on GitHub.
Citation: Heesch, R., Ehrhardt, J., Niggemann, O. (2024). Integrating Machine Learning into an SMT-Based Planning Approach for Production Planning in Cyber-Physical Production Systems. In: Nowaczyk, S., et al. Artificial Intelligence. ECAI 2023 International Workshops. ECAI 2023. Communications in Computer and Information Science, vol 1948. Springer, Cham. doi: 10.1007/978-3-031-50485-3_33.