Cognitive Capabilities for the CAAI in Cyber-Physical Production Systems

TL;DR

This paper introduces a cognitive architecture for AI in cyber-physical production systems that automates pipeline configuration, adapts models dynamically, and integrates various modules to improve efficiency and robustness.

Contribution

It presents a novel cognitive module for CAAI that automates AI pipeline orchestration, adaptation, and integration in cyber-physical production environments.

Findings

Successful implementation using Docker, Kubernetes, and Kafka.

Effective automatic pipeline configuration and adaptation.

Improved robustness and efficiency in real-world use case.

Abstract

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.