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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64391
Volume:
10 |
5 |
May 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computer Science
Acropolis Institute Of Technology and Research
Department of Computer Science
Acropolis Institute Of Technology and Research
Department of Computer Science
Acropolis Institute Of Technology and Research
Department of Computer Science
Acropolis Institute Of Technology and Research
Department of Computer Science
Acropolis Institute Of Technology and Research
Department of Computer Science
Acropolis Institute Of Technology and Research
This paper presents a decentralised intelligent automation system using Fetch.AI, which integrates Artificial Intelligence (AI), Machine Learning (ML), and Blockchain technology. The system utilises Autonomous Economic Agents (AEAs) capable of independently performing tasks such as data analysis, negotiation, and transaction execution without continuous human intervention. Unlike traditional centralized systems, the proposed approach ensures transparency, security, and trust through blockchain integration. The research explores the architecture, functionalities, and real-world applications of Fetch.AI in domains such as smart mobility, supply chain management, and energy optimization. The results demonstrate improved efficiency, reduced operational costs, and enhanced automation, highlighting the potential of decentralized AI systems in building a smart digital economy.
KEYWORDS
This project demonstrates the successful design and implementation of a decentralized intelligent automation system using Fetch.AI. By integrating Artificial Intelligence, Machine Learning, and Blockchain technology, the system enables Autonomous Economic Agents (AEAs) to perform tasks independently, make intelligent decisions, and interact securely without continuous human intervention. The proposed system overcomes the limitations of traditional centralized architectures by ensuring transparency, security, and trust through decentralized operations. The use of blockchain technology guarantees tamper-proof transactions, while machine learning enhances predictive analysis and efficient resource utilization. The implementation showcases how autonomous agents can communicate, negotiate, and execute tasks in real-time, leading to improved system performance and reduced operational costs. Furthermore, the project highlights the wide applicability of decentralized AI systems in areas such as smart cities, supply chain management, energy optimization, and smart mobility. Despite certain challenges like system complexity and scalability issues, the overall results validate the effectiveness of the approach. This project establishes a strong foundation for future advancements in decentralized automation and intelligent systems, contributing to the development of a self-operating digital ecosystem and a smarter, more efficient technological future.
[1] Fetch.AI Foundation, “Fetch.AI Whitepaper,” 2019. [Online]. Available: https://Fetch.AI [2] Fetch.AI, “Fetch.AI Documentation,” 2023. [Online]. Available: https://docs.Fetch.AI [3] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Pearson, 2020. [4] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016. [5] Satoshi Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System,” 2008. [6] Ethereum Foundation, “Ethereum Whitepaper,” 2014. [7] IEEE, “Blockchain Technology Overview,” IEEE Xplore, 2021. [8] ACM, “Multi-Agent Systems: A Survey,” ACM Digital Library, 2019. [9] B. McMahan et al., “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Proc. AISTATS, 2017. [10] ] World Economic Forum, “Blockchain Beyond the Hype,” 2018. [11] IBM, “Blockchain for Supply Chain,” IBM Research Report, 2020. [12] Cosmos Network, “Cosmos Whitepaper,” 2016. [13] NVIDIA, “AI and Deep Learning Guide,” 2022. [14] MIT, “Artificial Intelligence Research Publications,” MIT CSAIL, 2023. [15] Stanford University, “AI Index Report,” Stanford HAI, 2023.
Publication Date
6/1/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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