IASelect: Finding Best-fit Agent Practices in Industrial CPS Using Graph Databases
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.
Cite
@article{arxiv.2108.01413,
title = {IASelect: Finding Best-fit Agent Practices in Industrial CPS Using Graph Databases},
author = {Chandan Sharma and Roopak Sinha and Paulo Leitao},
journal= {arXiv preprint arXiv:2108.01413},
year = {2021}
}
Comments
Conference paper, 7 pages, 6 figures, 1 table