Autoencoder Alignment Approach to Run-Time Interoperability for System of Systems Engineering

Abstract

We formulate the challenging problem to establish information interoperability within a system of systems (SoS) as a machine-learning task, where autoencoder embeddings are aligned using message data and metadata to automate message translation. An SoS requires communication and collaboration between otherwise independently operating systems, which are subject to different standards, changing conditions, and hidden assumptions. Thus, interoperability approaches that are based on standardization and symbolic inference will have limited generalization and scalability in the SoS engineering domain. We present simulation experiments performed with message data generated using heating and ventilation system simulations. While the unsupervised learning approach proposed here remains unsolved in general, we obtained up to 75% translation accuracy with autoencoders aligned by back-translation after investigating seven different models with different training protocols and hyperparameters. For comparison, we obtain 100% translation accuracy on the same task with supervised learning, but the need for a labeled dataset makes that approach less interesting. We discuss possibilities to extend the proposed unsupervised learning approach to reach higher translation accuracy.