Understanding Levels of Automation in Human-Machine Collaboration

Glaucia Melo, Nathalia Nascimento, Paulo Alencar, Donald Cowan

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Recent advances in software and artificial intelligence technologies have led to the increasing need to support the collaboration between humans and systems in various application domains. The growing capacity for systems has leveraged the power of building applications that have higher autonomy. However, a proper understanding of allocating tasks to either humans or machines is still lacking, and no suggestions are provided to support this allocation. Current approaches do not consider knowledge about the appropriate level of automation (LOA) in this collaboration and do not support adaptive automation, especially task assignments during the system's operation. The knowledge about which factors affect the variability in human-system interaction LOA has not been explicitly captured. This paper presents a preliminary study that identifies the factors that influence levels of automation in autonomous systems and present the identified factors as a list. Identifying the factors that influence the level of autonomy of systems advances research in the design of autonomous systems by introducing an adaptive automation approach that can recommend levels of automation to support human-computer interactions. Modern systems must be prepared to identify, capture and process the significant volume and variety of data related to the factors that might influence the variability of systems' behaviours.

    Original languageEnglish (US)
    Title of host publicationProceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
    EditorsShusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages3952-3958
    Number of pages7
    ISBN (Electronic)9781665480451
    DOIs
    StatePublished - 2022
    Event2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan
    Duration: Dec 17 2022Dec 20 2022

    Publication series

    NameProceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

    Conference

    Conference2022 IEEE International Conference on Big Data, Big Data 2022
    Country/TerritoryJapan
    CityOsaka
    Period12/17/2212/20/22

    All Science Journal Classification (ASJC) codes

    • Modeling and Simulation
    • Computer Networks and Communications
    • Information Systems
    • Information Systems and Management
    • Safety, Risk, Reliability and Quality
    • Control and Optimization

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