CAAI—a cognitive architecture to introduce artificial intelligence in cyber-physical production systems

• Published in: International journal of advanced manufacturing technology Vol. 111; no. 1-2; pp. 609 - 626
• Main Authors: Fischbach, Andreas, Strohschein, Jan, Bunte, Andreas, Stork, Jörg, Faeskorn-Woyke, Heide, Moriz, Natalia, Bartz-Beielstein, Thomas
• Format: Journal Article
• Language: English
• Published: London Springer London 01-11-2020; Springer Nature B.V
• Subjects: Algorithms; Architecture; Artificial intelligence; Big Data; CAE) and Design; Computer-Aided Engineering (CAD; Engineering; Industrial and Production Engineering; Mechanical Engineering; Media Management; Modular design; Original Article; Performance evaluation; Pipelines; Modularization; SMBO; Big data platform; Industry 4.0; Cognition; Reference architecture; AutoML; Artificial intelligence; Optimization; CPPS
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-020-06094-z

This paper introduces CAAI, a novel cognitive architecture for artificial intelligence in cyber-physical production systems. The goal of the architecture is to reduce the implementation effort for the usage of artificial intelligence algorithms. The core of the CAAI is a cognitive module that processes the user’s declarative goals, selects suitable models and algorithms, and creates a configuration for the execution of a processing pipeline on a big data platform. Constant observation and evaluation against performance criteria assess the performance of pipelines for many and different use cases. Based on these evaluations, the pipelines are automatically adapted if necessary. The modular design with well-defined interfaces enables the reusability and extensibility of pipeline components. A big data platform implements this modular design supported by technologies such as Docker, Kubernetes, and Kafka for virtualization and orchestration of the individual components and their communication. The implementation of the architecture is evaluated using a real-world use case. The prototypic implementation is accessible on GitHub and contains a demonstration.