New Metaverse Games Based on Artificial Intelligence: A Review
DOI:
https://doi.org/10.54536/ijm.v2i1.3230Keywords:
Metaverse, Gaming, Artificial Intelligence (AI), Virtual Reality (AR), Augmented Reality (AR), Extended Realities (XR)Abstract
The current review conveys Metaverse integration with AI-based gaming exploring the advancements in Metaverse games and its potential in growing self-learning AI in the latest years for the readers. Using state-of-the-art deep learning techniques, it aimed to pinpoint its advancements, issues, and suggested solutions. For the current review, 18 papers were used that appeared in peer-reviewed publications and were searchable on Google Scholar within the last five years (2020–2024). To analyse the collected data, thematic analysis was employed. The article delves into the various ways the Meta-Metaverse might be used in the gaming industry. It highlights how it can enhance character creation, game design, level design, and visual effects, among other areas. The entertainment industry’s usage of AI in game production encompasses a wide range of methods, such as CVEs, deep learning, and intrinsic curiosity-driven variation autoencoders.). The Metaverse, a dynamic platform, uses ML/DL algorithms for classification, clustering, and regression, while pre-trained AI models can achieve great responses quickly. These technologies streamline the gaming world and create interactive platforms for various applications. Deep Reinforcement Learning (DRL) is proposed as a dynamic solution to balance the stability of the game world, the intelligence of non-player characters, and the sustainability of the Metaverse environment.
References
Al-Azawi, R., Ayesh, A., Kenny, I., & Al-Masruri, K. (2012). Analysis of using intelligent techniques in games. In Proceedings of the 3rd GAMEON-ARABIA Conference (Muscat).
Bécue, A., Praça, I., & Gama, J. (2021). Artificial intelligence, cyber-threats and Industry 4.0: Challenges and opportunities. Artificial Intelligence Review, 54(5), 3849-3886.
Bibri, S. E., & Jagatheesaperumal, S. K. (2023). Harnessing the potential of the metaverse and artificial intelligence for the internet of city things: Cost-effective XReality and synergistic AIoT technologies. Smart Cities, 6(5), 2397-2429.
Chenna, S. (2023). Augmented reality and AI: Enhancing human-computer interaction in the metaverse. Social Science Research Network. https://doi.org/10.2139/ssrn.4324629
Cho, Y., Hong, S., Kim, M., & Kim, J. (2022). DAVE: Deep learning-based asymmetric virtual environment for immersive experiential metaverse content. Electronics, 11(16), 2604.
Das, S., Ghosh, L., & Saha, S. (2020). Analyzing gaming effects on cognitive load using artificial intelligent tools. In Proceedings of the 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE.
Du, X., Fuqian, X., Hu, J., Wang, Z., & Yang, D. (2021). Uprising E-sports industry: Machine learning/AI improvement in-game performance using deep reinforcement learning. In Proceedings of the 2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE).
Dyulicheva, Y. Y., & Glazieva, A. O. (2021). Game based learning with artificial intelligence and immersive technologies: An overview. CS&SE@ SW, 146-159.
Hadjar, H., McKevitt, P., & Hemmje, M. (2022). Home-based immersive web rehabilitation gaming with audiovisual sensors. In Proceedings of the 33rd European Conference on Cognitive Ergonomics.
Han, G.-J., Zhang, X.-F., Wang, H., & Mao, C.-G. (2020). Curiosity-driven variational autoencoder for deep Q network. In Advances in Knowledge Discovery and Data Mining: 24th Pacific-Asia Conference, PAKDD 2020, Singapore, May 11–14, 2020, Proceedings, Part I (Vol. 24). Springer.
Henz, P. (2022). The psychological impact of the Metaverse. Discover Psychology, 2(1), 47.
Himangi, P., & Singla, M. (2022). Investigating Role of Deep Learning in Metaverse. International Journal of New Practices in Management and Engineering, 11(01), 53-60.
Huynh-The, T., Pham, Q.-V., Pham, X.-Q., Nguyen, T. T., Han, Z., & Kim, D.-S. (2023). Artificial intelligence for the metaverse: A survey. Engineering Applications of Artificial Intelligence, 117, 105581.
Jamshidi, M., Dehghaniyan Serej, A., Jamshidi, A., & Moztarzadeh, O. (2023). The meta-metaverse: ideation and future directions. Future Internet, 15(8), 252.
Jeon, H., Youn, H., Ko, S., & Kim, T. (2022). Blockchain and AI Meet in the Metaverse. Advances in the Convergence of Blockchain and Artificial Intelligence, 73(10.5772).
Kadyr, S., & Tolganay, C. (2024). Affective computing methods for simulation of action scenarios in video games. Procedia Computer Science, 231, 341-346.
Kraus, S., Kanbach, D. K., Krysta, P. M., Steinhoff, M. M., & Tomini, N. (2022). Facebook and the creation of the metaverse: radical business model innovation or incremental transformation? International Journal of Entrepreneurial Behavior & Research, 28(9), 52-77.
Li, P., Zhang, Z., Al-Sumaiti, A. S., Werghi, N., & Yeun, C. Y. (2023). A robust adversary detection-deactivation method for metaverse-oriented collaborative deep learning. IEEE Sensors Journal.
Li, Y., Ma, Z., & Zhang, L. (2023). Research on interaction design based on artificial intelligence technology in a metaverse environment. In Proceedings of the International Conference on Human-Computer Interaction.
Lyu, Z. (2023). State-of-the-art human-computer-interaction in metaverse. International Journal of Human–Computer Interaction, 1-19.
Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 9(1), 381-386.
Navidi, N., & Landry Jr, R. (2021). New approach in human-ai interaction by reinforcement-imitation learning. Applied Sciences, 11(7), 3068.
Nimpattanavong, C., Taveekitworachai, P., Khan, I., Nguyen, T. V., Thawonmas, R., Choensawat, W., & Sookhanaphibarn, K. (2023). Am I fighting well? Fighting game commentary generation with ChatGPT. In Proceedings of the 13th International Conference on Advances in Information Technology.
Njoku, J. N., Nwakanma, C. I., Amaizu, G. C., & Kim, D. S. (2023). Prospects and challenges of Metaverse application in data‐driven intelligent transportation systems. IET Intelligent Transport Systems, 17(1), 1-21.
Taye, M. M. (2023). Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers, 12(5), 91.
Xiong, M. (2022). An Acquisition Deal in the Gaming World. Highlights in Business, Economics and Management, 2, 452-456.
Yin, Q.-Y., Yang, J., Huang, K.-Q., Zhao, M.-J., Ni, W.-C., Liang, B., Huang, Y., Wu, S., & Wang, L. (2023). AI in Human-computer Gaming: Techniques, Challenges and Opportunities. Machine Intelligence Research, 20(3), 299-317.
Zeng, Y. (2021). How human-centered AI will contribute towards intelligent gaming systems. In Proceedings of the AAAI Conference on Artificial Intelligence.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Omar Alotaibi
This work is licensed under a Creative Commons Attribution 4.0 International License.
