Towards augmenting cyber-physical-human collaborative cognition for human-automation interaction in complex manufacturing and operational environments

Jiao, Jianxin; Zhou, Feng; Gebraeel, Nagi; Duffy, Vincent G.

doi:10.1080/00207543.2020.1722324

Cited by 54 publications

(20 citation statements)

References 101 publications

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“…To impart the trustworthiness of IIoT data in smart manufacturing, several efforts have been made. For instance, trust between humans and machines is enforced using cyber-physical-human analysis [114], which enhances the trust in dynamic modeling, cognitive prediction, and optimized interaction between humans and machines. Some of the major security threats faced by IIoT devices/machines are shown in Fig.…”

Section: Industrial Iotmentioning

confidence: 99%

Explainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Jagatheesaperumal¹,

Pham

Ruby

et al. 2022

IEEE Open J. Commun. Soc.

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Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines. As the number of connected devices keeps on growing, the Internet of Things (IoT) market needs to be trustworthy for the end-users. However, existing literature still lacks a systematic and comprehensive survey work on the use of XAI for IoT. To bridge this lacking, in this paper, we address the XAI frameworks with a focus on their characteristics and support for IoT. We illustrate the widely-used XAI services for IoT applications, such as security enhancement, Internet of Medical Things (IoMT), Industrial IoT (IIoT), and Internet of City Things (IoCT). We also suggest the implementation choice of XAI models over IoT systems in these applications with appropriate examples and summarize the key inferences for future works. Moreover, we present the cutting-edge development in edge XAI structures and the support of sixth-generation (6G) communication services for IoT applications, along with key inferences. In a nutshell, this paper constitutes the first holistic compilation on the development of XAI-based frameworks tailored for the demands of future IoT use cases.

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Section: Industrial Iotmentioning

confidence: 99%

Explainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Jagatheesaperumal¹,

Pham

Ruby

et al. 2022

IEEE Open J. Commun. Soc.

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show abstract

“…5) Integrated security for maturity models: In augmentation with human intelligence, collaborative cognition has been adapted and applied to the developed models for appropriate decision making in industrial applications [195]. The maturity model includes cyber-physical-human analysis using novel strategies with an enhanced level of trust and an optimized level of human interactions and automation.…”

Section: Adversarial MLmentioning

confidence: 99%

The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review of Applications, Techniques, Challenges, and Future Research Directions

Jagatheesaperumal¹,

Rahouti²,

Ahmad³

et al. 2021

Preprint

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The increasing need for economic, safe, and sustainable smart manufacturing combined with novel technological enablers, has paved the way for Artificial Intelligence (AI) and Big Data in support of smart manufacturing. This implies a substantial integration of AI, Industrial Internet of Things (IIoT), Robotics, Big data, Blockchain, 5G communications, in support of smart manufacturing and the dynamical processes in modern industries. In this paper, we provide a comprehensive overview of different aspects of AI and Big Data in Industry 4.0 with a particular focus on key applications, techniques, the concepts involved, key enabling technologies, challenges, and research perspective towards deployment of Industry 5.0. In detail, we highlight and analyze how the duo of AI and Big Data is helping in different applications of Industry 4.0. We also highlight key challenges in a successful deployment of AI and Big Data methods in smart industries with a particular emphasis on data-related issues, such as availability, bias, auditing, management, interpretability, communication, and different adversarial attacks and security issues. In a nutshell, we have explored the significance of AI and Big data towards Industry 4.0 applications through panoramic reviews and discussions. We believe, this work will provide a baseline for future research in the domain.

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“…Field studies are accompanied by various data collection techniques to elicit relevant content on work domain characteristics and user activity/mental models 3 . The techniques commonly used involve observations, interviews, focus groups, process mapping, card sorting, fieldwork experimentation with SMEs [63,12,54,58,64].…”

Section: Inferring Explanation Needs Through User Studiesmentioning

confidence: 99%

“…However, as computer systems are increasingly integrated into work environments, new forms of Human-AI collaboration emerge [3,4]. Achieving business goals in partnership with artificial agents raises new social, technical, and operational challenges [2,5,6,7].…”

Section: Introductionmentioning

confidence: 99%

Towards an Explanation Space to Align Humans and Explainable-AI Teamwork

Cabour,

Morales,

Ledoux

et al. 2021

Preprint

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Providing meaningful and actionable explanations to end-users is a fundamental prerequisite for implementing explainable intelligent systems in the real world. Explainability is a situated interaction between a user and the AI system rather than being static design principles. The content of explanations is context-dependent and must be defined by evidence about the user and its context. This paper seeks to operationalize this concept by proposing a formative architecture that defines the explanation space from a user-inspired perspective. The architecture comprises five intertwined components to outline explanation requirements for a task: (1) the end-users mental models, (2) the end-users cognitive process, (3) the user interface, (4) the human-explainer agent, and the (5) agent process. We first define each component of the architecture. Then we present the Abstracted Explanation Space, a modeling tool that aggregates the architecture's components to support designers in systematically aligning explanations with the end-users work practices, needs, and goals. It guides the specifications of what needs to be explained (content -end-users mental model), why this explanation is necessary (context -end-users cognitive process), to delimit how to explain it (format -human-explainer agent and user interface), and when should the explanations be given. We then exemplify the tool's use in an ongoing case study in the aircraft maintenance domain. Finally, we discuss possible contributions of the tool, known limitations/areas for improvement, and future work to be done.

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Towards augmenting cyber-physical-human collaborative cognition for human-automation interaction in complex manufacturing and operational environments

Cited by 54 publications

References 101 publications

Explainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Explainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review of Applications, Techniques, Challenges, and Future Research Directions

Towards an Explanation Space to Align Humans and Explainable-AI Teamwork

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