IASelect: Finding Best-fit Agent Practices in Industrial CPS Using Graph Databases

TL;DR

This paper introduces IASelect, a graph database-based tool that helps industry practitioners identify the most suitable industrial agent practices for complex cyber-physical systems in the context of the Fourth Industrial Revolution.

Contribution

The paper presents the design and development of IASelect, a novel tool that enables flexible, visual querying of a repository of industrial agent practices for ICPS.

Findings

IASelect allows interactive identification of best-fit practices.

The tool supports visual querying without manual coding.

It enhances decision-making in industrial cyber-physical systems.

Abstract

The ongoing fourth Industrial Revolution depends mainly on robust Industrial Cyber-Physical Systems (ICPS). ICPS includes computing (software and hardware) abilities to control complex physical processes in distributed industrial environments. Industrial agents, originating from the well-established multi-agent systems field, provide complex and cooperative control mechanisms at the software level, allowing us to develop larger and more feature-rich ICPS. The IEEE P2660.1 standardisation project, "Recommended Practices on Industrial Agents: Integration of Software Agents and Low Level Automation Functions" focuses on identifying Industrial Agent practices that can benefit ICPS systems of the future. A key problem within this project is identifying the best-fit industrial agent practices for a given ICPS. This paper reports on the design and development of a tool to address this challenge. This tool, called IASelect, is built using graph databases and provides the ability to flexibly and visually query a growing repository of industrial agent practices relevant to ICPS. IASelect includes a front-end that allows industry practitioners to interactively identify best-fit practices without having to write manual queries.