  This paper presents the cognitive module of the cognitive architecture for
artificial intelligence (CAAI) in cyber-physical production systems (CPPS). The
goal of this architecture is to reduce the implementation effort of artificial
intelligence (AI) algorithms in CPPS. Declarative user goals and the provided
algorithm-knowledge base allow the dynamic pipeline orchestration and
configuration. A big data platform (BDP) instantiates the pipelines and
monitors the CPPS performance for further evaluation through the cognitive
module. Thus, the cognitive module is able to select feasible and robust
configurations for process pipelines in varying use cases. Furthermore, it
automatically adapts the models and algorithms based on model quality and
resource consumption. The cognitive module also instantiates additional
pipelines to test algorithms from different classes. CAAI relies on
well-defined interfaces to enable the integration of additional modules and
reduce implementation effort. Finally, an implementation based on Docker,
Kubernetes, and Kafka for the virtualization and orchestration of the
individual modules and as messaging-technology for module communication is used
to evaluate a real-world use case.
