TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Strohschein, Jan
A1  - Fischbach, Andreas
A1  - Bunte, Andreas
A1  - Faeskorn-Woyke, Heide
A1  - Moriz, Natalia
A1  - Bartz-Beielstein, Thomas
T1  - Cognitive capabilities for the CAAI in cyber-physical production systems
JF  - The International Journal of Advanced Manufacturing Technology
N2  - 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 evaluate algorithms from different classes on test functions. 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.
KW  - Kognition
KW  - Cognition
KW  - Industry 4.0
KW  - Big Data Platform
KW  - Machine Learning
KW  - CPPS
KW  - Optimization
KW  - Algorithm Selection
KW  - Simulation
KW  - Maschinelles Lernen
Y1  - 2021
UN  - https://nbn-resolving.org/urn:nbn:de:hbz:832-epub4-21232
SN  - 0268-3768
SS  - 0268-3768
SN  - 1433-3015
SS  - 1433-3015
U6  - https://doi.org/10.1007/s00170-021-07248-3
DO  - https://doi.org/10.1007/s00170-021-07248-3
VL  - 115
IS  - 11-12
SP  - 3513
EP  - 3532
S1  - 20
PB  - Springer London
ER  -