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, creating such domain descriptions manually is considered difficult, tedious, and error-prone. Therefore, we propose a novel generic planning approach, which can integrate mathematical formulas or Machine Learning models into a symbolic SMT-based planning algorithm, thus 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.