# Interoperability and machine-to-machine translation model with mappings to machine learning tasks

## TL;DR

This paper proposes a mathematical framework for semantic translation in cyber-physical systems, enabling interoperability across diverse information models and standards using machine learning techniques.

## Contribution

It introduces a translator-based interoperability model with formal definitions and explores mappings to machine learning tasks for automatic semantic translation.

## Key findings

- Mathematical formulation of translator learning tasks
- Mappings to machine learning solutions for semantic translation
- Discussion on learning translators without common physical context

## Abstract

Modern large-scale automation systems integrate thousands to hundreds of thousands of physical sensors and actuators. Demands for more flexible reconfiguration of production systems and optimization across different information models, standards and legacy systems challenge current system interoperability concepts. Automatic semantic translation across information models and standards is an increasingly important problem that needs to be addressed to fulfill these demands in a cost-efficient manner under constraints of human capacity and resources in relation to timing requirements and system complexity. Here we define a translator-based operational interoperability model for interacting cyber-physical systems in mathematical terms, which includes system identification and ontology-based translation as special cases. We present alternative mathematical definitions of the translator learning task and mappings to similar machine learning tasks and solutions based on recent developments in machine learning. Possibilities to learn translators between artefacts without a common physical context, for example in simulations of digital twins and across layers of the automation pyramid are briefly discussed.