Abstract
Robots are expected to be more intelligent in consuming digital infrastructures during the process of continual learning. The future of connected Robotics should be skillful in maximizing Quality of Experience (QoE) for its vertical users rather than solely reacting to Quality of Service (QoS). The paper provides a detailed use case specification and a network softwarization paradigm for realizing the 6G vision of connected intelligence. It serves as a guidance for developing future network applications to ground the idea of the connected intelligence.
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References
Sefidcon, A., et al.: The network compute fabric – advancing digital transformation with ever-present service continuity. Ericsson Technol. Rev. 2021(7), 2–11 (2021). https://doi.org/10.23919/ETR.2021.9904679(2021)
Lesort, T., et al.: Continual learning for robotics: definition, framework, learning strategies, opportunities and challenges. Inf. Fusion 58, 52–68 (2020), ISSN 1566-2535, https://doi.org/10.1016/j.inffus.2019.12.004
Qiu, R., et al.: Intent-based deployment for robot applications in the 5g-enabled non-public-network. ITU J. Future Evolving Technol. 4(1), 209–220 (2023). https://doi.org/10.52953/AYMI1991
5GASP, “5GASP definition of a NetApp” (2021). https://community.5gasp.eu/index.php/2021/07/12/what-is-a-netapp/
Bessem, S., et al.: Network applications: opening up 5G and beyond networks. Zenodo (2022). https://doi.org/10.5281/zenodo.7123919
5G-ERA, 5G-ERA GitHub Repository (2023). https://github.com/5g-era
Hospedales, T., et al.: Meta-learning in neural networks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 01, 1, 5555 (2021). https://doi.org/10.1109/TPAMI.2021.3079209
Bekkum, et al.: Modular design patterns for hybrid learning and reasoning systems a taxonomy, patterns and use cases. Appl. Intell. 51, 6528–6546 (2021). Doi: https://doi.org/10.1007/s10489-021-02394-3
Sun, R., et al.: Artificial intelligence: connectionist and symbolic approaches. In: International Encyclopedia of the Social & Behavioral Sciences (2000). https://doi.org/10.1016/B0-08-043076-7/00553-2
Quigley, M., et.al.: ROS: an open-source Robot Operating System. In: Workshops at the IEEE International Conference on Robotics and Automation (2009)
ETSI OSM, Open source Management and Orchestration (MANO) https://osm.etsi.org/
Acknowledgement
The work in this paper is sponsored by the EU H2020 project “5G Enhance Robot Autonomy” under grant agreement number 101016681.
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Qiu, R., Li, D., Liu, E., Lessi, C.C., Agapiou, G., Gavrielides, A. (2023). Use Cases for Network Applications to Enable Connected Intelligence. In: Maglogiannis, I., Iliadis, L., Papaleonidas, A., Chochliouros, I. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 677. Springer, Cham. https://doi.org/10.1007/978-3-031-34171-7_13
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DOI: https://doi.org/10.1007/978-3-031-34171-7_13
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