Multi-agent systems and decentralized artificial superintelligence
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Abstract (arXiv)
Multi-agents systems communication is a technology, which provides a way for multiple interacting intelligent agents to communicate with each other and with environment. Multiple-agent systems are used to solve problems that are difficult for solving by individual agent. Multiple-agent communication technologies can be used for management and organization of computing fog and act as a global, distributed operating system. In present publication we suggest technology, which combines decentralized P2P BOINC general-purpose computing tasks distribution, multiple-agents communication protocol and smart-contract based rewards, powered by Ethereum blockchain. Such system can be used as distributed P2P computing power market, protected from any central authority. Such decentralized market can further be updated to system, which learns the most efficient way for software-hardware combinations usage and optimization. Once system learns to optimize software-hardware efficiency it can be updated to general-purpose distributed intelligence, which acts as combination of single-purpose AI.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Feb 2017.
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Citing papers per year: 2022: 2; 2023: 0; 2024: 1; 2025: 0; 2026: 1.
4 papers.
- 2024A Review on Building Blocks of Decentralized Artificial Intelligencecited by 2
- 2022Dynamic Noises of Multi-Agent Environments Can Improve Generalization: Agent-based Models meets Reinforcement Learningcited by 0
- 2022ADDAI: Anomaly Detection using Distributed AIcited by 0
- 2026Artificial Intelligence and Innovation Ecosystem: Evolutionary Developments, Challenges, and Future Directionscited by 0
- 2026Artificial Intelligence and Innovation Ecosystem: Evolutionary Developments, Challenges, and Future Directions27 Jul 2026
- 2024A Review on Building Blocks of Decentralized Artificial Intelligence5 Feb 2024
- 2022ADDAI: Anomaly Detection using Distributed AI2 May 2022
- 2022Dynamic Noises of Multi-Agent Environments Can Improve Generalization: Agent-based Models meets Reinforcement Learning26 Mar 2022