Patented intelligence: Cloning human decision models for Industry 4.0

Terziyan, Vagan; Gryshko, Svitlana; Golovianko, Mariia

doi:10.1016/j.jmsy.2018.04.019

Cited by 73 publications

(46 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this context, Terziyan et al introduce Pi-Mind technology as a mixture of human-expert-driven decision-making and AI-driven decision-making approaches for smart manufacturing processes based on AI and ML technologies. In many cases, this hybrid approach outperforms rational decision-making processes by human operators [42]. Venkatapathy et al and Zeidler et al describe a basic concept of a hybrid network for the usage of human and machine synergies in the intra-logistics.…”

Section: Hybrid Decision Support Systemsmentioning

confidence: 99%

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Woschank¹,

Rauch

Zsifkovits³

2020

Sustainability

196

View full text Add to dashboard Cite

Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.

show abstract

Section: Hybrid Decision Support Systemsmentioning

confidence: 99%

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Woschank¹,

Rauch

Zsifkovits³

2020

Sustainability

196

View full text Add to dashboard Cite

show abstract

“…To further improve the accuracy and the quality of industrial processes, the Intelligent Factory Space (IFS), an AI-inspired architecture that facilitates the interaction between humans and robots, was presented in Reference [93], emphasising on the safety and trust aspects, during the two-way communication between humans and robots. Also, the notion of patented intelligence (Pi)-Mind was described in detail in Reference [94], as an Intelligence-as-a-Service (IaaS) provider that transparently and proactively introduces human expertise on decision-making based on self-awareness. As the dynamic industrial CPS usually encompasses unexpected or emergency situations, conventional ML and predictive methods might not lead to the desired outcome.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

241

View full text Add to dashboard Cite

The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.

show abstract

“…This process of feedback and interactions increases the efficiency of both the system and the human mentor and allows the human operator to develop trust in the system over time. A technology called Pi-Mind suggests a compromise between total human control and total AI control over decisions, bringing a middle layer of them (Terziyan et al 2018).…”

Section: Decision Support Based On "Collaboration"mentioning

confidence: 99%

“…We recommend that emerging MDS technologies should preserve access to subtle cues by alternative and novel means. Although the previously mentioned Pi-Mind technology (Terziyan et al 2018) attempts to capture the human creative cognitive capabilities, it does not consider provisions for implicit learning. Only a few studies have focused on this possibility.…”

Section: Mds Should Not Hinder Implicit Learning About Machine Behaviormentioning

confidence: 99%

Collaborating AI and human experts in the maintenance domain

Illankoon

Tretten

2020

AI & Soc

View full text Add to dashboard Cite

Maintenance decision errors can result in very costly problems. The 4th industrial revolution has given new opportunities for the development of and use of intelligent decision support systems. With these technological advancements, key concerns focus on gaining a better understanding of the linkage between the technicians’ knowledge and the intelligent decision support systems. The research reported in this study has two primary objectives. (1) To propose a theoretical model that links technicians’ knowledge and intelligent decision support systems, and (2) to present a use case how to apply the theoretical model. The foundation of the new model builds upon two main streams of study in the decision support literature: “distribution” of knowledge among different agents, and “collaboration” of knowledge for reaching a shared goal. This study resulted in the identification of two main gaps: firstly, there must be a greater focus upon the technicians’ knowledge; secondly, technicians need assistance to maintain their focus on the big picture. We used the cognitive fit theory, and the theory of distributed situation awareness to propose the new theoretical model called “distributed collaborative awareness model.” The model considers both explicit and implicit knowledge and accommodates the dynamic challenges involved in operational level maintenance. As an application of this model, we identify and recommend some technological developments required in augmented reality based maintenance decision support.

show abstract

Patented intelligence: Cloning human decision models for Industry 4.0

Cited by 73 publications

References 29 publications

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Collaborating AI and human experts in the maintenance domain

Contact Info

Product

Resources

About