## 1 Introduction  
As the integration of hardware and software continues to evolve, production systems are becoming increasingly intricate, now often referred to as Cyber-Physical Production Systems (CPPS). In particular, Artificial Intelligence (AI) can be instrumental in improving processes such as anomaly detection, optimization, or predictive maintenance. However, at present, incorporating AI algorithms into these systems is far from straightforward; it demands substantial time, financial resources, and expertise. Adopting a standardized architecture could facilitate the integration of AI technologies, especially for small and medium-sized enterprises, empowering them to remain competitive. For modern production processes, AI methods are used for various purposes. These include optimization, predictive maintenance, anomaly detection, and condition monitoring. The Cognitive Architecture for Artificial Intelligence (CAAI) was developed because the existing architectures from the domain of automation--i.e., the Reference Architecture Model Industrie 4.0 (RAMI4.0) [1], the Industrial Internet Reference Architecture (IIRA) [2], and the 5C architecture [3]--are too abstract. They do not define implementation details,such as interfaces. Other cognitive architectures based on cognitive psychology, such as Soar [4] or ACT-R [5], lack generalizability. The KOARCH project endeavored to address the gap of crafting an architecture characterized by a low level of abstraction while maintaining the highest possible degree of generalizability, simultaneously. The CAAI was introduced in [6] as a novel cognitive architecture for AI in CPPS. The goal of the architecture was to reduce the implementation effort for the usage of AI algorithms. The core of the CAAI is a cognitive module that processes the user's declarative goals, selects suitable models and algorithms, and creates a configuration for the execution of a processing pipeline on a Big Data Platform (BDP). Constant observation and evaluation against performance criteria assess the performance of pipelines for many different use cases. Based on these evaluations, the pipelines are automatically adapted if necessary. The modular design with well-defined interfaces enables the reusability and extensibility of pipeline components. A BDP implements this modular design, supported by technologies such as Docker, Kubernetes, and Kafka, for virtualization and orchestration of the individual components and their communication. The implementation of the architecture is evaluated using a real-world use case. The prototypic implementation is accessible on GitHub and contains a demonstration ([[https://github.com/janstrohschein/KOARCH](https://github.com/janstrohschein/KOARCH)]([https://github.com/janstrohschein/KOARCH](https://github.com/janstrohschein/KOARCH)), accessed on 16 October 2023). During the retrospective evaluation of the CAAI, it became apparent that the architecture in the use case for CPPS provides a suitable environment for the implementation of Online Machine Learning (OML) algorithms. This is caused by the continuous data streams produced by the system's machines. In an OML set up, these enormous amounts of data would not need to be stored. Furthermore, the algorithms could be continuously updated and, thus, react better to structural changes within the data. For this reason, this article focuses on evaluating whether OML algorithms are a significant enhancement to the existing architecture. Furthermore, the possibilities of surrogate-model-based hyperparameter tuning in the context of OML algorithms will be explored. This article is structured as follows: Section 2 begins with a summary and a brief retrospective evaluation of the CAAI. In addition, this section introduces the concept of OML and establishes a framework for performing experiments that compare Batch Machine Learning (BML) and OML. Following this, we detail the concept of Surrogate-Model-Based Optimization (SMBO), which we use to determine the best hyperparameters for the respective OML algorithms.