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Part I: Managing the Industry 4.0 revolution
Implementing Industry 4.0
The Need for a Holistic Approach
Motivation in a business company using technology-based communication
Decision Support in Everyday Business using Self-Enforcing Networks
Approaches for Cognitive Assistance in Industry 4.0
Part II: Modelling for Industry 4.0
Modelling and Simulation in Industry 4.0
Homogenization algorithm based on incremental L2-discrepancy filtering for data-driven modelling
Linking Industry 4.0, Learning Factory and Simulation: testbeds and proof-of-concept experiments
Part III: Machine Learning in Industry 4.0
Intelligent Digital Twin system in the semiconductors manufacturing industry
Automatic defect recognition in nonwovens using images and metadata analysis
a deep learning approach
MIRAI: A Modifiable, Interpretable, and Rational AI decision support system
Multi-Agent Reinforcement Learning for the Energy Optimization of Cyber-Physical Production Systems
Affect of artificial intelligence technologies and digitalisation on jurisprudence and education
Part IV: Agents in Industry 4.0
Approach for model driven development of multi agent systems for ambient intelligence
Designing trust in highly automated virtual assistants : A taxonomy of levels of autonomy
Introducing the concept of "digital-agent signatures": How SSI can be expanded for the needs of Industry 4.0.

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