Metaversal intelligence: Unifying human-AI interactions in human-in-the-loop AIB-Metaverse

The convergence of 6G and Web 3 technologies with the Metaverse necessitates user-centric, intelligent solutions to address challenges of adaptability, scalability, and efficiency in decentralized systems. This paper introduces the Human-AI Blockchain (HAIB) Metaverse architecture and the Intelligen...

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Bibliographic Details
Published in:Computer networks (Amsterdam, Netherlands : 1999) Vol. 269; p. 111425
Main Authors: Soltanshahi, Minoo, Maier, Martin
Format: Journal Article
Language:English
Published: Elsevier B.V 01.09.2025
Subjects:
Blockchain
Extended stigmergy
HAIB-Metaverse architecture
Intelligent smart contract
Reinforcement learning algorithms
Web 3
Intelligent smart contract
HAIB-Metaverse architecture
Extended stigmergy
Web 3
Blockchain
Reinforcement learning algorithms
ISSN:1389-1286
Online Access:Get full text
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Summary:The convergence of 6G and Web 3 technologies with the Metaverse necessitates user-centric, intelligent solutions to address challenges of adaptability, scalability, and efficiency in decentralized systems. This paper introduces the Human-AI Blockchain (HAIB) Metaverse architecture and the Intelligent Smart Contract (InSC) framework, which integrate Reinforcement Learning (RL) and extended stigmergy to optimize multi-agent interactions and enable dynamic, self-executing smart contracts. The HAIB architecture enhances human-AI collaboration through a human-in-the-loop approach, facilitating adaptive decision-making and efficient resource allocation. Serving as the cognitive core, the InSC framework combines dynamic environmental feedback with real-time data analytics to enable intelligent decision-making. Experimental evaluations demonstrate that the proposed framework reduces gas consumption by 32–33% while increasing cumulative rewards. These findings highlight the significant potential of intelligent smart contracts in advancing decentralized intelligence within the Metaverse.
ISSN:1389-1286
DOI:10.1016/j.comnet.2025.111425