A Cognitive Assistant for Operators: AI-Powered Knowledge Sharing on Complex Systems

被引:7
|
作者
Freire, Samuel Kernan [1 ]
Panicker, Sarath Surendranadha [2 ]
Ruiz-Arenas, Santiago [3 ]
Rusak, Zoltan [1 ]
Niforatos, Evangelos [1 ]
机构
[1] Delft Univ Technol, NL-2628 CD Delft, Netherlands
[2] Cognizant Technol Solut, NL-1096 BK Amsterdam, Netherlands
[3] Univ EAFIT, Medellin 3300, Antioquia, Colombia
基金
欧盟地平线“2020”;
关键词
Production facilities; Artificial intelligence; Training; Manufacturing; Machine components; Cameras; Best practices; TACIT KNOWLEDGE;
D O I
10.1109/MPRV.2022.3218600
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Operating a complex and dynamic system, such as an agile manufacturing line, is a knowledge-intensive task. It imposes a steep learning curve on novice operators and prompts experienced operators to continuously discover new knowledge, share it, and retain it. In practice, training novices is resource-intensive, and the knowledge discovered by experts is not shared effectively. To tackle these challenges, we developed an AI-powered pervasive system that provides cognitive augmentation to users of complex systems. We present an AI cognitive assistant that provides on-the-job training to novices while acquiring and sharing (tacit) knowledge from experts. Cognitive support is provided as dialectic recommendations for standard work instructions, decision-making, training material, and knowledge acquisition. These recommendations are adjusted to the user and context to minimize interruption and maximize relevance. In this article, we describe how we implemented the cognitive assistant, how it interacts with users, its usage scenarios, and the challenges and opportunities.
引用
收藏
页码:50 / 58
页数:9
相关论文
共 50 条
  • [41] Effect of AI Explanations on Human Perceptions of Patient-Facing AI-Powered Healthcare Systems
    Zhang, Zhan
    Genc, Yegin
    Wang, Dakuo
    Ahsen, Mehmet Eren
    Fan, Xiangmin
    JOURNAL OF MEDICAL SYSTEMS, 2021, 45 (06)
  • [42] Contextual knowledge sharing and cooperation in intelligent assistant systems
    Brézillon, P
    Pomerol, JC
    TRAVAIL HUMAIN, 1999, 62 (03): : 223 - 246
  • [43] Geo-semantic-parsing: AI-powered geoparsing by traversing semantic knowledge graphs
    Nizzoli, Leonardo
    Avvenuti, Marco
    Tesconi, Maurizio
    Cresci, Stefano
    DECISION SUPPORT SYSTEMS, 2020, 136
  • [44] AI-Powered E-Learning for Lifelong Learners: Impact on Performance and Knowledge Application
    Ahn, Hyun Yong
    SUSTAINABILITY, 2024, 16 (20)
  • [45] Transition from Traditional Knowledge Retrieval into AI-Powered Knowledge Retrieval in Infrastructure Projects: A Literature Review
    Boamah, Fredrick Ahenkora
    Jin, Xiaohua
    Senaratne, Sepani
    Perera, Srinath
    INFRASTRUCTURES, 2025, 10 (02)
  • [46] AI-Powered Knowledge Base Enables Transparent Prediction of Nanozyme Multiple Catalytic Activity
    Razlivina, Julia
    Dmitrenko, Andrei
    Vinogradov, Vladimir
    JOURNAL OF PHYSICAL CHEMISTRY LETTERS, 2024, 15 (22): : 5804 - 5813
  • [47] AI-powered Electronic Control Systems for Software-defined Agricultural Machines
    Purkrabek, Arno
    ATZheavy Duty Worldwide, 2023, 16 (02) : 20 - 25
  • [48] Ethical and legal implications of using AI-powered recommendation systems in streaming services
    Sorban, Kinga
    INFORMACIOS TARSADALOM, 2021, 21 (02): : 63 - 82
  • [49] Deep learning anomaly detection in AI-powered intelligent power distribution systems
    Duan, Jing
    FRONTIERS IN ENERGY RESEARCH, 2024, 12
  • [50] Not Merely Useful but Also Amusing: Impact of Perceived Usefulness and Perceived Enjoyment on the Adoption of AI-Powered Coding Assistant
    Kim, Young Woo
    Cha, Min Chul
    Yoon, Sol Hee
    Lee, Seul Chan
    INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION, 2024,