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Decentralized Intelligence in GameFi: Embodied AI Agents and the Convergence of DeFi and Virtual Ecosystems

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  • Jia, Fernando
  • Zheng, Jade
  • Li, Florence

Abstract

In the rapidly evolving landscape of GameFi, a fusion of gaming and decentralized finance (DeFi), there exists a critical need to enhance player engagement and economic interaction within gaming ecosystems. Our GameFi ecosystem aims to fundamentally transform this landscape by integrating advanced embodied AI agents into GameFi platforms. These AI agents, developed using cutting-edge large language models (LLMs), such as GPT-4 and Claude AI, are capable of proactive, adaptive, and contextually rich interactions with players. By going beyond traditional scripted responses, these agents become integral participants in the game's narrative and economic systems, directly influencing player strategies and in-game economies. We address the limitations of current GameFi platforms, which often lack immersive AI interactions and mechanisms for community engagement or creator monetization. Through the deep integration of AI agents with blockchain technology, we establish a consensus-driven, decentralized GameFi ecosystem. This ecosystem empowers creators to monetize their contributions and fosters democratic collaboration among players and creators. Furthermore, by embedding DeFi mechanisms into the gaming experience, we enhance economic participation and provide new opportunities for financial interactions within the game. Our approach enhances player immersion and retention and advances the GameFi ecosystem by bridging traditional gaming with Web3 technologies. By integrating sophisticated AI and DeFi elements, we contribute to the development of more engaging, economically robust, and community-centric gaming environments. This project represents a significant advancement in the state-of-the-art in GameFi, offering insights and methodologies that can be applied throughout the gaming industry.

Suggested Citation

  • Jia, Fernando & Zheng, Jade & Li, Florence, 2025. "Decentralized Intelligence in GameFi: Embodied AI Agents and the Convergence of DeFi and Virtual Ecosystems," OSF Preprints tn5rx, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:tn5rx
    DOI: 10.31219/osf.io/tn5rx
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    1. Biais, Bruno & Glosten, Larry & Spatt, Chester, 2005. "Market microstructure: A survey of microfoundations, empirical results, and policy implications," Journal of Financial Markets, Elsevier, vol. 8(2), pages 217-264, May.
    2. Guillermo Angeris & Hsien-Tang Kao & Rei Chiang & Charlie Noyes & Tarun Chitra, 2019. "An analysis of Uniswap markets," Papers 1911.03380, arXiv.org, revised Feb 2021.
    3. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    4. Fabian Schär, 2021. "Decentralized Finance: On Blockchain- and Smart Contract-Based Financial Markets," Review, Federal Reserve Bank of St. Louis, vol. 103(2), pages 153-174, April.
    5. Chen, Yan & Bellavitis, Cristiano, 2020. "Blockchain disruption and decentralized finance: The rise of decentralized business models," Journal of Business Venturing Insights, Elsevier, vol. 13(C).
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