AHCI RESEARCH GROUP

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proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

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2024

Liew, Z. Q.; Xu, M.; Lim, W. Y. Bryan; Niyato, D.; Kim, D. I.

AI-Generated Bidding for Immersive AIGC Services in Mobile Edge-Empowered Metaverse Proceedings Article

In: Int. Conf. Inf. Networking, pp. 305–309, IEEE Computer Society, 2024, ISBN: 19767684 (ISSN); 979-835033094-6 (ISBN).

Abstract | Links | BibTeX | Tags: Artificial intelligence generated bid, Artificial intelligence generated content, Bidding mechanism, Bidding models, Budget constraint, Budget control, Budget-constraint bidding, Constrained optimization, Content services, Immersive, Learning systems, Metaverses, Mobile edge computing, Reinforcement Learning, Semantics, Virtual tour

@inproceedings{liew_ai-generated_2024,

title = {AI-Generated Bidding for Immersive AIGC Services in Mobile Edge-Empowered Metaverse},

author = {Z. Q. Liew and M. Xu and W. Y. Bryan Lim and D. Niyato and D. I. Kim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198324990&doi=10.1109%2fICOIN59985.2024.10572159&partnerID=40&md5=271f5c45e8e95f01b42acaee89599bd5},

doi = {10.1109/ICOIN59985.2024.10572159},

isbn = {19767684 (ISSN); 979-835033094-6 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Int. Conf. Inf. Networking},

pages = {305–309},

publisher = {IEEE Computer Society},

abstract = {Recent advancements in Artificial Intelligence Generated Content (AIGC) provide personalized and immersive content generation services for applications such as interactive advertisements, virtual tours, and metaverse. With the use of mobile edge computing (MEC), buyers can bid for the AIGC service to enhance their user experience in real-time. However, designing strategies to optimize the quality of the services won can be challenging for budget-constrained buyers. The performance of classical bidding mechanisms is limited by the fixed rules in the strategies. To this end, we propose AI-generated bidding (AIGB) to optimize the bidding strategies for AIGC. AIGB model uses reinforcement learning model to generate bids for the services by learning from the historical data and environment states such as remaining budget, budget consumption rate, and quality of the won services. To obtain quality AIGC service, we propose a semantic aware reward function for the AIGB model. The proposed model is tested with a real-world dataset and experiments show that our model outperforms the classical bidding mechanism in terms of the number of services won and the similarity score. © 2024 IEEE.},

keywords = {Artificial intelligence generated bid, Artificial intelligence generated content, Bidding mechanism, Bidding models, Budget constraint, Budget control, Budget-constraint bidding, Constrained optimization, Content services, Immersive, Learning systems, Metaverses, Mobile edge computing, Reinforcement Learning, Semantics, Virtual tour},

pubstate = {published},

tppubtype = {inproceedings}

}