A framework for AI-based self-adaptive cyber-physical process systems. Guldner, A., Hoffmann, M., Lohr, C., Machhamer, R., Malburg, L., Morgen, M., Rodermund, S. C., Schäfer, F., Schaupeter, L., Schneider, J., Theusch, F., Bergmann, R., Dartmann, G., Kuhn, N., Naumann, S., Timm, I. J., Vette-Steinkamp, M., & Weyers, B. it - Information Technology, 2023.
Paper doi abstract bibtex 3 downloads Digital transformation is both an opportunity and a challenge. To take advantage of this opportunity for humans and the environment, the transformation process must be understood as a design process that affects almost all areas of life. In this paper, we investigate AI-Based Self-Adaptive Cyber-Physical Process Systems (AI-CPPS) as an extension of the traditional CPS view. As contribution, we present a framework that addresses challenges that arise from recent literature. The aim of the AI-CPPS framework is to enable an adaptive integration of IoT environments with higher-level process-oriented systems. In addition, the framework integrates humans as actors into the system, which is often neglected by recent related approaches. The framework consists of three layers, i.e., processes, semantic modeling, and systems and actors, and we describe for each layer challenges and solution outlines for application. We also address the requirement to enable the integration of new networked devices under the premise of a targeted process that is optimally designed for humans, while profitably integrating AI and IoT. It is expected that AI-CPPS can contribute significantly to increasing sustainability and quality of life and offer solutions to pressing problems such as environmental protection, mobility, or demographic change. Thus, it is all the more important that the systems themselves do not become a driver of resource consumption.
@article{Guldner_AICPPS_2023,
url = {https://doi.org/10.1515/itit-2023-0001},
title = {A framework for AI-based self-adaptive cyber-physical process systems},
author = {Achim Guldner and Maximilian Hoffmann and Christian Lohr and Rüdiger Machhamer and Lukas Malburg and Marlies Morgen and Stephanie C. Rodermund and Florian Schäfer and Lars Schaupeter and Jens Schneider and Felix Theusch and Ralph Bergmann and Guido Dartmann and Norbert Kuhn and Stefan Naumann and Ingo J. Timm and Matthias Vette-Steinkamp and Benjamin Weyers},
journal = {it - Information Technology},
volume = {65},
number = {3},
url = {http://www.wi2.uni-trier.de/shared/publications/2023_Guldner_AICPPS.pdf},
doi = {doi:10.1515/itit-2023-0001},
keywords = {{Artificial Intelligence, Business Process Managemenet, Cyber-Physical Systems, Framework, Green AI, Process-Aware Information System}},
abstract = {{Digital transformation is both an opportunity and a challenge. To take advantage of this opportunity for humans and the environment, the transformation process must be understood as a design process that affects almost all areas of life. In this paper, we investigate AI-Based Self-Adaptive Cyber-Physical Process Systems (AI-CPPS) as an extension of the traditional CPS view. As contribution, we present a framework that addresses challenges that arise from recent literature. The aim of the AI-CPPS framework is to enable an adaptive integration of IoT environments with higher-level process-oriented systems. In addition, the
framework integrates humans as actors into the system, which is often neglected by recent related approaches. The framework consists of three layers, i.e., processes, semantic modeling, and systems and actors, and we describe for each layer challenges and solution outlines for application. We also address the requirement to enable the integration of new networked devices under the premise of a targeted process that is optimally designed for humans, while profitably integrating AI and IoT. It is expected that AI-CPPS can contribute significantly to increasing sustainability and quality of life and offer solutions to pressing problems such as environmental protection, mobility, or demographic change. Thus, it is all the more important that the systems themselves do not become a driver of resource consumption.}},
year = {2023}
}
Downloads: 3
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