AHCI RESEARCH GROUP

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Papers published in international journals,
proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

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2024

Ding, P.; Liu, J.; Sun, M.; Li, L.; Liu, H.

Enhancing Computational Processing Performance for Generative AI Large Models with Autonomous Decision-Making in Metaverse Applications Proceedings Article

In: Proc. - IEEE Int. Conf. Metaverse Comput., Netw., Appl., MetaCom, pp. 253–258, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-833151599-7 (ISBN).

Abstract | Links | BibTeX | Tags: Adversarial machine learning, AGI (Artificial General Intelligence), Artificial general intelligence, Artificial general intelligences, Autonomous decision, Autonomous Decision-Making, Data assimilation, Data integration, Decisions makings, Digital Twin Technology, Emotion Recognition, Generative adversarial networks, Generative AI large model, Generative AI Large Models, Large models, Metaverse, Metaverses, Model Acceleration, Model Compression, Multi agent systems, Multi-agent systems, Multi-modal data, Multi-Modal Data Integration, Multiagent systems (MASs), Reinforcement Learning, Reinforcement learnings, Spatio-temporal data

@inproceedings{ding_enhancing_2024,

title = {Enhancing Computational Processing Performance for Generative AI Large Models with Autonomous Decision-Making in Metaverse Applications},

author = {P. Ding and J. Liu and M. Sun and L. Li and H. Liu},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211489063&doi=10.1109%2fMetaCom62920.2024.00048&partnerID=40&md5=ae085a7d90b12c9090f5bf7a274bc7ce},

doi = {10.1109/MetaCom62920.2024.00048},

isbn = {979-833151599-7 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. - IEEE Int. Conf. Metaverse Comput., Netw., Appl., MetaCom},

pages = {253–258},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {We explore how to enhance the computational processing performance for generative AI large models with autonomous decision-making in metaverse applications. We first introduce the relationship between AI large models and the Metaverse. We elaborate on the application scenarios of generative AI large models in Metaverse, including real-time weather simulation, embodied intelligence of agents, dynamic environment interaction, and user emotion recognition. We then propose the method of Multi-Dimensional Optimization Generation Framework (MDOGF) to improve computational processing performance. The experiment results show great improvement in computational processing performance. © 2024 IEEE.},

keywords = {Adversarial machine learning, AGI (Artificial General Intelligence), Artificial general intelligence, Artificial general intelligences, Autonomous decision, Autonomous Decision-Making, Data assimilation, Data integration, Decisions makings, Digital Twin Technology, Emotion Recognition, Generative adversarial networks, Generative AI large model, Generative AI Large Models, Large models, Metaverse, Metaverses, Model Acceleration, Model Compression, Multi agent systems, Multi-agent systems, Multi-modal data, Multi-Modal Data Integration, Multiagent systems (MASs), Reinforcement Learning, Reinforcement learnings, Spatio-temporal data},

pubstate = {published},

tppubtype = {inproceedings}

}