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Why Symbolic AI is a Key Technology for Self-Adaption in the Context of CPPS

Why Symbolic AI is a Key Technology for Self-Adaption in the Context of CPPS

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.

Evaluation of Cognitive Architectures for Cyber-Physical Production Systems

Evaluation of Cognitive Architectures for Cyber-Physical Production Systems

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.

Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning

Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning

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

Improving NeuroEvolution Efficiency by Surrogate Model-Based Optimization with Phenotypic Distance Kernels

Improving NeuroEvolution Efficiency by Surrogate Model-Based Optimization with Phenotypic Distance Kernels

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

Evaluation of Cognitive Architectures for Cyber-Physical Production Systems

Evaluation of Cognitive Architectures for Cyber-Physical Production Systems

Bunte, Andreas; Fischbach, Andreas; Strohschein, Jan; Bartz-Beielstein, Thomas; Faeskorn-Woyke, Heide; Niggemann, Oliver: Evaluation of Cognitive Architectures for Cyber-Physical ProductionSystems. In: arXiv e-prints Feb 2019. (BibTeX) (Paper)

 

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.

Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models

Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models

Bunte, Andreas; Stein, Benno; Niggemann, Oliver: Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models. Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), Hawaii, USA, Jan 2019. (BibTex) (Paper)

 

Abstract: This paper introduces a novel approach to Model-Based Diagnosis (MBD) for hybrid technical systems. Unlike existing approaches which normally rely on qualitative diagnosis models expressed in logic, our approach applies a learned quantitative model that is used to derive residuals. Based on these residuals a diagnosis model is generated and used for a root cause identification. The new solution has several advantages such as the easy integration of new machine learning algorithms into MBD, a seamless integration of qualitative models, and a significant speed-up of the diagnosis runtime. The paper at hand formally defines the new approach, outlines its advantages and drawbacks, and presents an evaluation with real-world use cases.

Integrating OWL Ontologies for Smart Services into AutomationML and OPC UA

Integrating OWL Ontologies for Smart Services into AutomationML and OPC UA

Bunte, Andreas; Niggemann, Oliver; Stein, BennoIntegrating OWL Ontologies for Smart Services into AutomationML and OPC UA. In: 23th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Sep 2018. (BibTeX) (Paper)

 

Abstract: This work shows how OWL ontologies can be represented into the automation standards AutomationML and OPC UA. It is often asserted that an integration is possible, but no detailed review could be found. The integration of OWL into the standards is relevant, because it enables the collection and usage of data through the whole life cycle in OWL. We show that it is possible, but we identified some restriction regarding the representation in OPC UA.