---
res:
  bibo_abstract:
  - Cyber-Physical Systems (CPS) play an essential role in today’s production processes,
    leveraging Artificial Intelligence (AI) to enhance operations such as optimization,
    anomaly detection, and predictive maintenance. This article reviews a cognitive
    architecture for Artificial Intelligence, which has been developed to establish
    a standard framework for integrating AI solutions into existing production processes.
    Given that machines in these processes continuously generate large streams of
    data, Online Machine Learning (OML) is identified as a crucial extension to the
    existing architecture. To substantiate this claim, real-world experiments using
    a slitting machine are conducted, to compare the performance of OML to traditional
    Batch Machine Learning. The assessment of contemporary OML algorithms using a
    real production system is a fundamental innovation in this research. The evaluations
    clearly indicate that OML adds significant value to CPS, and it is strongly recommended
    as an extension of related architectures, such as the cognitive architecture for
    AI discussed in this article. Additionally, surrogate-model-based optimization
    is employed, to determine the optimal hyperparameter settings for the corresponding
    OML algorithms, aiming to achieve peak performance in their respective tasks.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Alexander
      foaf_name: Hinterleitner, Alexander
      foaf_surname: Hinterleitner
  - foaf_Person:
      foaf_givenName: Richard
      foaf_name: Schulz, Richard
      foaf_surname: Schulz
  - foaf_Person:
      foaf_givenName: Lukas
      foaf_name: Hans, Lukas
      foaf_surname: Hans
  - foaf_Person:
      foaf_givenName: Aleksandr
      foaf_name: Subbotin, Aleksandr
      foaf_surname: Subbotin
  - foaf_Person:
      foaf_givenName: Nils
      foaf_name: Barthel, Nils
      foaf_surname: Barthel
  - foaf_Person:
      foaf_givenName: Noah
      foaf_name: Pütz, Noah
      foaf_surname: Pütz
  - foaf_Person:
      foaf_givenName: Martin
      foaf_name: Rosellen, Martin
      foaf_surname: Rosellen
  - foaf_Person:
      foaf_givenName: Thomas
      foaf_name: Bartz-Beielstein, Thomas
      foaf_surname: Bartz-Beielstein
  - foaf_Person:
      foaf_givenName: Christoph
      foaf_name: Geng, Christoph
      foaf_surname: Geng
      foaf_workInfoHomepage: http://www.librecat.org/personId=61408
  - foaf_Person:
      foaf_givenName: Phillip
      foaf_name: Priss, Phillip
      foaf_surname: Priss
  bibo_doi: 10.3390/app132011506
  bibo_issue: '20'
  bibo_volume: 13
  dct_date: 2023^xs_gYear
  dct_identifier:
  - UT:001096019200001
  dct_isPartOf:
  - http://id.crossref.org/issn/2076-3417
  dct_language: eng
  dct_publisher: MDPI AG@
  dct_subject:
  - machine learning
  - online algorithms
  - cyber-physical production systems
  - surrogate-based optimization
  dct_title: Online Machine Learning and Surrogate-Model-Based Optimization for Improved
    Production Processes Using a Cognitive Architecture@
...