Cognitive capabilities for the CAAI in cyber-physical production systems

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 t...

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Bibliographic Details
Published inInternational journal of advanced manufacturing technology Vol. 115; no. 11-12; pp. 3513 - 3532
Main Authors Strohschein, Jan, Fischbach, Andreas, Bunte, Andreas, Faeskorn-Woyke, Heide, Moriz, Natalia, Bartz-Beielstein, Thomas
Format Journal Article
LanguageEnglish
Published London Springer London 01.08.2021
Springer Nature B.V
Subjects
Algorithms
Artificial intelligence
CAE) and Design
Computer-Aided Engineering (CAD
Configurations
Engineering
Industrial and Production Engineering
Knowledge bases (artificial intelligence)
Mechanical Engineering
Media Management
Modules
Original Article
Performance evaluation
Pipelines
Simulation
Big data platform
Machine learning
Algorithm selection
Cognition
Industry 4.0
Optimization
CPPS
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Summary: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.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-021-07248-3