Learning Process Steps as Dynamical Systems for a Sub-Symbolic Approach of Process Planning in Cyber-Physical Production Systems

Approaches in AI planning for Cyber-Physical Production Systems (CPPS) are mainly symbolic and depend on comprehensive formalizations of system domains and planning problems. Handcrafting such formalizations requires detailed knowledge of the formalization language, of the CPPS, and is overall considered difficult, tedious, and error-prone. Within this paper, we suggest a sub-symbolic approach for solving planning problems in CPPS. Our approach relies on neural networks that learn the dynamical behavior of individual process steps from global time-series observations of the CPPS and are embedded in a superordinate network architecture. In this context, we present the process step representation network architecture (peppr), a novel neural network architecture, which can learn the behavior of individual or multiple dynamical systems from global time-series observations. We evaluate peppr on real datasets from physical and biochemical CPPS, as well as artificial datasets from electrical and mathematical domains. Our model outperforms baseline models like multilayer perceptrons and variational autoencoders and can be considered as a first step towards a sub-symbolic approach for planning in CPPS.