@inproceedings{4509, author = {{Bunte, Andreas and Richter, Frank and Diovisalvi, Rosanna}}, booktitle = {{International Conference on Agents and Artificial Intelligence (ICAART)}}, location = {{Online Streaming}}, title = {{{Why it is Hard to Find AI in SMEs - A Survey From the Practice and How to Promote it}}}, year = {{2021}}, } @misc{12800, abstract = {{his 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.}}, author = {{Strohschein, Jan and Fischbach, Andreas and Bunte, Andreas and Faeskorn-Woyke, Heide and Moriz, Natalia and Bartz-Beielstein, Thomas}}, booktitle = {{The International Journal of Advanced Manufacturing Technology}}, issn = {{1433-3015}}, keywords = {{Cognition, Industry 40, Big data platform, Machine learning, CPPS, Optimization, Algorithm selection, Simulation}}, number = {{11-12}}, pages = {{3513--3532}}, publisher = {{Springer }}, title = {{{Cognitive capabilities for the CAAI in cyber-physical production systems}}}, doi = {{10.1007/s00170-021-07248-3}}, volume = {{115}}, year = {{2021}}, } @inproceedings{4512, author = {{Geng, Christoph and Moriz, Natalia and Bunte, Andreas and Trsek, Henning}}, booktitle = {{KommA 2020 – Jahreskolloquium Kommunikation in der Automation}}, location = {{Lemgo}}, title = {{{Concept for Rule-Based Information Aggregation in Modular Production Plants}}}, year = {{2020}}, } @inproceedings{4513, author = {{Bunte, Andreas and Ressler, Henrik and Moriz, Natalia}}, booktitle = {{25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) }}, location = {{Wien}}, title = {{{Automated Detection of Production Cycles in Production Plants using Machine Learning}}}, year = {{2020}}, } @article{4518, 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.}}, author = {{Fischbach, Andreas and Strohschein, Jan and Bunte, Andreas and Stork, Jörg and Faeskorn-Woyke, Heide and Moriz, Natalia and Bartz-Beielstein, Thomas}}, issn = {{1433-3015}}, journal = {{The International Journal of Advanced Manufacturing Technology}}, keywords = {{CPPS, Artificial intelligence, Industry 40, Reference architecture, Optimization, SMBO, Cognition, Big data platform, Modularization, AutoML}}, number = {{1/2}}, pages = {{609--626}}, publisher = {{Springer}}, title = {{{CAAI -- A Cognitive Architecture to Introduce Artificial Intelligence in Cyber-Physical Production Systems}}}, doi = {{10.1007/s00170-020-06094-z}}, volume = {{111}}, year = {{2020}}, } @inbook{4786, author = {{Niggemann, Oliver and Biswas, Gautam and Kinnebrew, John S. and Khorasgani, Hamed and Hranisavljevic, Nemanja and Bunte, Andreas}}, booktitle = {{Handbuch Industrie 4.0: Produktion, Automatisierung und Logistik}}, editor = {{ten Hompel, Michael and Vogel-Heuser, Birgit and Bauernhansl, Thomas}}, isbn = {{978-3-662-45537-1}}, pages = {{800}}, publisher = {{Springer}}, title = {{{Konzeptualisierung als Kernfrage des Maschinellen Lernens in der Produktion}}}, year = {{2020}}, } @inproceedings{4780, author = {{Bunte, Andreas and Wunderlich, Paul and Moriz, Natalia and Li, Peng and Mankowski, Andre and Rogalla, Antje and Niggemann, Oliver}}, booktitle = {{24nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}}, title = {{{Why Symbolic AI is a Key Technology for Self-Adaption in the Context of CPPS}}}, year = {{2019}}, } @inproceedings{4781, 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.}}, author = {{Bunte, Andreas and Fischbach, Andreas and Strohschein, Jan and Bartz-Beielstein, Thomas and Faeskorn-Woyke, Heide and Niggemann, Oliver}}, booktitle = {{24nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}}, isbn = {{978-1-7281-0303-7}}, issn = {{1946-0759}}, keywords = {{Reference Architecture, Cognition, Industrie 4.0}}, location = {{Zaragoza, SPAIN}}, pages = {{729--736}}, publisher = {{IEEE}}, title = {{{Evaluation of Cognitive Architectures for Cyber-Physical Production Systems}}}, doi = {{10.1109/etfa.2019.8869038}}, year = {{2019}}, } @article{4784, author = {{Bunte, Andreas and Fischbach, Andreas and Strohschein, Jan and Bartz-Beielstein, Thomas and Faeskorn-Woyke, Heide and Niggemann, Oliver}}, journal = {{arXiv e-prints}}, title = {{{Evaluation of Cognitive Architectures for Cyber-Physical ProductionSystems}}}, year = {{2019}}, } @inproceedings{4785, author = {{Bunte, Andreas and Stein, Benno and Niggemann, Oliver}}, publisher = {{Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)}}, title = {{{Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models}}}, year = {{2019}}, } @inproceedings{4787, author = {{Bunte, Andreas and Niggemann, Oliver and Stein, Benno}}, booktitle = {{23th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}}, title = {{{Integrating OWL Ontologies for Smart Services into AutomationML and OPC UA}}}, year = {{2018}}, } @inproceedings{4788, author = {{Bunte, Andreas and Li, Peng and Niggemann, Oliver}}, booktitle = {{International Conference on Agents and Artificial Intelligence (ICAART)}}, publisher = {{SCITEPRESS}}, title = {{{Mapping Data Sets to Concepts Using Machine Learning and a Knowledge Based Approach}}}, year = {{2018}}, } @inproceedings{4789, author = {{Bunte, Andreas and Li, Peng and Niggemann, Oliver}}, booktitle = {{3rd Conference on Machine Learning for Cyber Physical Systems and Industry 4.0 (ML4CPS)}}, title = {{{Learned Abstraction: Knowledge Based Concept Learning for Cyber Physical Systems}}}, year = {{2017}}, } @inproceedings{4782, abstract = {{Die zunehmende Individualisierung von Produkten stellt neue Anforderun- gen an Produktionsanlagen, wodurch deren Komplexit¨at steigt. Damit komplexe An- lagen effizient bedient werden k¨onnen, vor allem im Fall eines Fehlers, sind neue Inter- aktionskonzepte erforderlich. In dieser Arbeit wurde ein Konzept f¨ur die Sprachverar- beitung entworfen und prototypisch implementiert. Zus¨atzlich zur Sprachverarbeitung ist eine semantische Schicht notwendig um eine Steuerung ¨uber Sprache zu realisieren}}, author = {{Bunte, Andreas and Diedrich, Alexander and Niggemann, Oliver}}, booktitle = {{Tagungsband des Entwicklerforums "HMI – Komponenten Lösungen"}}, publisher = {{HMI}}, title = {{{Natürlichsprachliche Schnittstelle für Produktionssysteme}}}, year = {{2016}}, } @inproceedings{4790, author = {{Bunte, Andreas and Diedrich, Alexander and Niggemann, Oliver}}, booktitle = {{International Workshop on the Principles of Diagnosis (DX)}}, title = {{{Semantics Enable Standardized User Interfaces for Diagnosis in Modular Production Systems}}}, year = {{2016}}, } @inproceedings{4791, author = {{Bunte, Andreas and Diedrich, Alexander and Niggemann, Oliver}}, booktitle = {{21th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}}, title = {{{Integrating Semantics for Diagnosis of Manufacturing Systems}}}, year = {{2016}}, } @inbook{4792, author = {{Niggemann, Oliver and Biswas, Gautam and Khorasgani, Hamed and Volgmann, Sören and Bunte, Andreas}}, booktitle = {{Handbuch Industrie 4.0 Bd. 2}}, editor = {{Vogel-Hauser, Birgit and Bauernhansl, Thomas and ten Hompel, Michael}}, publisher = {{Springer Berlin Heidelberg}}, title = {{{Datenanalyse in der intelligenten Fabrik}}}, year = {{2016}}, } @inproceedings{4793, author = {{Diedrich, Alexander and Bunte, Andreas and Maier, Alexander and Niggemann, Oliver}}, booktitle = {{Machine Learning for Cyber Physical Systems and Industry 4.0 (ML4CPS)}}, title = {{{Kognitive Architektur zum Konzeptlernen in technischen Systemen}}}, year = {{2015}}, } @inproceedings{4794, author = {{Niggemann, Oliver and Biswas, Gautam and Kinnebrew, John S. and Khorasgani, Hamed and Volgmann, Sören and Bunte, Andreas}}, booktitle = {{International Workshop on the Principles of Diagnosis (DX)}}, title = {{{Data-Driven Monitoring of Cyber-Physical Systems Leveraging on Big Data and the Internet-of-Things for Diagnosis and Control}}}, year = {{2015}}, } @inproceedings{4795, author = {{Niggemann, Oliver and Windmann, Stefan and Volgmann, Sören and Bunte, Andreas and Stein, Benno}}, booktitle = {{International Workshop on the Principles of Diagnosis (DX)}}, publisher = {{Graz, Austria}}, title = {{{Using Learned Models for the Root Cause Analysis of Cyber-Physical Production Systems}}}, year = {{2014}}, }