Evaluation of Cognitive Architectures for Cyber-Physical Production Systems

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.

Cognitive capabilities for the CAAI in cyber-physical production systems

“…The result is a blueprint for a domain-specific class of systems and facilitates communication and knowledge transfer. We evaluated proposed reference architectures for the application of AI in CPPS from the field of automation and cognitive sciences in previous work [7] and proposed CAAI [15] as an alternative that was more focused on software development for the application of AI in CPPS. Thus, we build on previous work, e.g., [46] and [19], to develop a three-tier architecture that enables an adaptable system with rapidly changing configurations.…”

“…This is shown in Fig. 2, on an energy optimization use case of a bakery, which is described in more detail in [7]. The goal of the use case is the minimization of the peak power consumption.…”

New Trends in the Use of Artificial Intelligence for the Industry 4.0

“…These systems are conceived as components in the production system able of executing physical processes in cooperation with other entities. Systems can adapt independently to changing circumstances, by learning from the additional information coming from the sensors [6].…”

“…The challenge motivating this research is to define a human-centred architecture to design, implement and evaluate cognitive advisor agents in the framework of a human cyber-physical production system (H-CPPS) [2,6] which supports the operator in Industry 4.0 to accomplish their job into an automation system [7] in a more efficient and effective form. The proposed overall H-CPPS architecture will be evaluated through a proof of concept based on a multi-agent system (MAS) implementing a cognitive robot (embodied agent) to assist the operator (operator 4.0) in a collaborative work with a cobot.…”

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

“…Cognitive architectures, such as Adaptive Control of Thought-Rational (ACT-R) [2] and Soar [25], implement certain concepts to reach adaption and cognitive capabilities. They can not be directly used to address industrial use cases, because they focus on cognition and lack generality [8]. Automated Machine Learning (AutoML) [14] and hyperheuristics can choose and configure a suitable algorithm automatically.…”