Jan Strohschein, Andreas Fischbach, Andreas Bunte, Heide Faeskorn-Woyke, Natalia Moriz and Thomas Bartz-Beielstein: Cognitive capabilities for the CAAI in cyber-physical production systems. In: Int J Adv Manuf Technol (2021). (Paper)

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.

Strohschein, Jan; Fischbach, Andreas; Bunte, Andreas; Faeskorn-Woyke, Heide; Moriz, Natalia; Bartz-Beielstein, Thomas: Cognitive Capabilities for the CAAI in Cyber-Physical Production Systems. In: arXiv e-prints, Dez 2020. (Paper)

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.

Fischbach, Andreas ; Strohschein, Jan; Bunte, Andreas; Stork, Jörg; Faeskorn-Woyke, Heide; Moriz, Natalia; Bartz-Beielstein, Thomas: CAAI - A Cognitive Architecture to Introduce Artificial Intelligence in Cyber-Physical Production Systems. In:Int J Adv Manuf Technol 111, 609–626 (2020). (Paper)

Abstract: This paper introduces CAAI, a novel cognitive architecture for artificial intelligence in cyber-physical production systems. The goal of the architecture is to reduce the implementation effort for the usage of artificial intelligence 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. Constant observation and evaluation against performance criteria assess the performance of pipelines for many and 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 big data platform 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.

Andreas Fischbach, Thomas Bartz-Beielstein: Improving the reliability of test functions generators; in: Applied Soft Computing, Volume 92, 2020, 106315, ISSN 1568-4946. (Paper)

Abstract: Computational intelligence methods have gained importance in several real-world domains such as process optimization, system identification, data mining, or statistical quality control. Tools are missing, which determine the performance of computational intelligence methods in these application domains in an objective manner. Statistics provide methods for comparing algorithms on certain data sets. In the past, several test suites were presented and considered as state of the art. However, there are several drawbacks of these test suites, namely: (i) problem instances are somehow artificial and have no direct link to real-world settings; (ii) since there is a fixed number of test instances, algorithms can be fitted or tuned to this specific and very limited set of test functions; (iii) statistical tools for comparisons of several algorithms on several test problem instances are relatively complex and not easily to analyze. We propose a methodology to overcome these difficulties. It is based on standard ideas from statistics: analysis of variance and its extension to mixed models. This paper combines essential ideas from two approaches: problem generation and statistical analysis of computer experiments.

Fischbach, Andreas ; Strohschein, Jan; Bunte, Andreas; Stork, Jörg; Faeskorn-Woyke, Heide; Moriz, Natalia; Bartz-Beielstein, Thomas: CAAI - A Cognitive Architecture to Introduce Artificial Intelligence in Cyber-Physical Production Systems. In: arXiv e-prints, Feb 2020. (Paper)

Abstract: This paper introduces CAAI, a novel cognitive architecture for artificial intelligence in cyber-physical production systems. The goal of the architecture is to reduce the implementation effort for the usage of artificial intelligence algorithms. The core of the CAAI is a cognitive module that processes declarative goals of the user, selects suitable models and algorithms, and creates a configuration for the execution of a processing pipeline on a big data platform. Constant observation and evaluation against performance criteria assess the performance of pipelines for many and varying 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 big data platform 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.

Strohschein J., Lara-Palma A., Faeskorn-Woyke H.: Detecting emotions in social media. A technological challenge to enhance youngest behavior. In: 28th AEDEM International Conference - Management in a Smart Society: business and technological challenges, Sep 2019.

Abstract: Social networks are everywhere and a large part of users even frequents more than one platform . "Due to a constant presence in the lives of their users, social networks have a decidedly strong social impact". The usability of these tools has been tested in multiple fields being beneficial in a large number of indicators, such as learning , inclusion or socialization. But several studies also suggest that social media usage is not beneficial for users health with symptoms ranging from sleep deprivation to anxiety and depression. Regarding these emotional consequences, Yoon, Kleinman, Mertz and Brannick in their meta-analysis study the correlation between social networks and symptoms of depression, highlight “Our results are consistent with the notion of ‘Facebook depression phenomenon’ and with the theoretical importance of social comparisons as an explanation”. But the effects of sleep deprivation and depression also persist during the workday and companies fear for their organizational productivity, some even try to ban social media completely.
This paper uses an automated approach to study the emotions of a larger group of social media users on Twitter over time. It is possible to extract emotions from the text of their status updates as shown by Tasoulis et al. and Colneric and Demsar. This analysis is based on the work of Colneric and Demsar and investigates if the emotions of users or groups of users become more negative over time as suggested by other studies.

Bunte, Andreas; Wunderlich, Paul; Moriz, Natalia; Li, Peng; Mankowski, Andre; Rogalla, Antje; Niggemann, Oliver: Why Symbolic AI is a Key Technology for Self-Adaption in the Context of CPPS. In: 24nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Zaragoza, Spain, Sep 2019. (BibTeX)

Abstract: The vision of smart factories are self-diagnosing, self-optimizing and self-adapting Cyber-Physical Production Systems (CPPS). Self-adaption, on which this paper focuses on, means that the CPPS can adapt itself to a changing environment, so that the downtime costs can be reduced by using the system modules most efficient. An architecture is introduced and demonstrated on a concrete use case to show how this capability can be achieved by using different Artificial Intelligence (AI) techniques. For each technique, we define challenges that have to be solved to use it in a real world environment. Additionally, we illustrate the symbolic and subsymbolic AI and argue why symbolic AI is an important aspect in the context of CPPS.

Bunte, Andreas; Fischbach, Andreas; Strohschein, Jan; Bartz-Beielstein, Thomas; Faeskorn-Woyke, Heide; Niggemann, Oliver: Evaluation of Cognitive Architectures for Cyber-Physical Production Systems. In: 24nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Zaragoza, Spain, Sep 2019. (BibTeX)

Abstract: Cyber-physical production systems (CPPS) integrate physical and computational resources due to increasingly available sensors and processing power. This enables the usage of data, to create additional benefit, such as condition monitoring or optimization. These capabilities can lead to cognition, such that the system is able to adapt independently to changing circumstances by learning from additional sensors information. Developing a reference architecture for the design of CPPS and standardization of machines and software interfaces is crucial to enable compatibility of data usage between different machine models and vendors. This paper analysis existing reference architecture regarding their cognitive abilities, based on requirements that are derived from three different use cases. The results from the evaluation of the reference architectures, which include two instances that stem from the field of cognitive science, reveal a gap in the applicability of the architectures regarding the generalizability and the level of abstraction. While reference architectures from the field of automation are suitable to address use case specific requirements, and do not address the general requirements, especially w.r.t. adaptability, the examples from the field of cognitive science are well usable to reach a high level of adaption and cognition. It is desirable to merge advantages of both classes of architectures to address challenges in the field of CPPS in Industrie 4.0.

Stork, Jörg; Zaefferer, Martin; Bartz-Beielstein; Thomas and Eiben, A. E.: Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning. In: Proceedings of the Genetic and Evolutionary Computation Conference, (GECCO '19), Prague, Czech Republic, 934-942, http://doi.acm.org/10.1145/3321707.3321829

Stork, Jörg; Zaefferer, Martin; Bartz-Beielstein, Thomas: Improving NeuroEvolution Efficiency by Surrogate Model-Based Optimization with Phenotypic Distance Kernels. In: Applications of Evolutionary Computation, Paul Kaufmann and Pedro A. Castillo (Eds.). Springer International Publishing, Cham, 504–519, April 2019