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

Bunte, Andreas; Wunderlich, Paul; Moriz, Natalia; Mankowski, André; Rogalla, Antje; Niggemann, Oliver

doi:10.1109/etfa.2019.8869082

Cited by 8 publications

(6 citation statements)

References 25 publications

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“…One vision of Industry 4.0 are cyber-physical production systems (CPPS) that are self-diagnosing, self-adapting and self-optimizing [1]. During production, these systems generate large amounts of multivariate data, which contain information about the systems' behaviour.…”

Section: Introductionmentioning

confidence: 99%

Accessing and Interpreting OPC UA Event Traces based on Semantic Process Descriptions

Tom¹,

Hranisavljevic²,

Fay³

2022

Preprint

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The analysis of event data from production systems is the basis for many applications associated with Industry 4.0. However, heterogeneous and disjoint data is common in this domain. As a consequence, contextual information of an event might be incomplete or improperly interpreted which results in suboptimal analysis results. This paper proposes an approach to access a production systems' event data based on the event data's context (such as the product type, process type or process parameters). The approach extracts filtered event logs from a database system by combining: 1) a semantic model of a production system's hierarchical structure, 2) a formalized process description and 3) an OPC UA information model. As a proof of concept we demonstrate our approach using a sample server based on OPC UA for Machinery Companion Specifications.

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Section: Introductionmentioning

confidence: 99%

Accessing and Interpreting OPC UA Event Traces based on Semantic Process Descriptions

Tom¹,

Hranisavljevic²,

Fay³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This inflexibility is not acceptable, because there is a great demand in the market for fast adapting CPPS, which can not be fulfilled by the approach of specific AI solutions. The CAAI has to be flexible and extendable to enable a quick reaction on this market demand [10]. (G-3) Generalizability: The CAAI should apply to many types of CPPS and support many different use cases.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the decision must be applied to the CPPS controller, which adapts the physical machine. For realization, several existing approaches, such as skill-based engineering, can be used [10,30,49].…”

Section: Introductionmentioning

confidence: 99%

CAAI—a cognitive architecture to introduce artificial intelligence in cyber-physical production systems

Fischbach

Strohschein

Bunte

et al. 2020

Int J Adv Manuf Technol

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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.

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“…This inflexibility is not acceptable, because there is a great demand in the market for fast adapting CPPS, which can not be fulfilled by the approach of specific AI solutions. The CAAI has to be flexible and extendable to enable a quick reaction on this market demand [8]. (G-3) Generalizability: The CAAI should apply to many types of CPPS and support many different use cases.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CAAI -- A Cognitive Architecture to Introduce Artificial Intelligence in Cyber-Physical Production Systems

Fischbach,

Strohschein,

Bunte

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

This paper introduces CAAI, a novel cognitive architecture for artificial intelligence in cyberphysical 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.

show abstract

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

Cited by 8 publications

References 25 publications

Accessing and Interpreting OPC UA Event Traces based on Semantic Process Descriptions

Accessing and Interpreting OPC UA Event Traces based on Semantic Process Descriptions

CAAI—a cognitive architecture to introduce artificial intelligence in cyber-physical production systems

CAAI -- A Cognitive Architecture to Introduce Artificial Intelligence in Cyber-Physical Production Systems

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