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Dynamic Storage Optimization for Communication between AI Agents

Author

Listed:
  • Andrei Tara

    (Department of Computer and Electrical Engineering, Lucian Blaga University of Sibiu, 10 Victoriei, 550024 Sibiu, Romania)

  • Hjalmar K. Turesson

    (Schulich School of Business, York University, North York, ON M3J 1P3, Canada)

  • Nicolae Natea

    (Openfabric Network SRL, 16, Iosif Velceanu, 550057 Sibiu, Romania)

Abstract

Today, AI is primarily narrow, meaning that each model or agent can only perform one task or a narrow range of tasks. However, systems with broad capabilities can be built by connecting multiple narrow AIs. Connecting various AI agents in an open, multi-organizational environment requires a new communication model. Here, we develop a multi-layered ontology-based communication framework. Ontology concepts provide semantic definitions for the agents’ inputs and outputs, enabling them to dynamically identify communication requirements and build processing pipelines. Critical is that the ontology concepts are stored on a decentralized storage medium, allowing fast reading and writing. The multi-layered design offers flexibility by dividing a monolithic ontology model into semantic layers, allowing for the optimization of read and write latencies. We investigate the impact of this optimization by benchmarking experiments on three decentralized storage mediums—IPFS, Tendermint Cosmos, and Hyperledger Fabric—across a wide range of configurations. The increased read-write speeds allow AI agents to communicate efficiently in a decentralized environment utilizing ontology principles, making it easier for AI to be used widely in various applications.

Suggested Citation

  • Andrei Tara & Hjalmar K. Turesson & Nicolae Natea, 2024. "Dynamic Storage Optimization for Communication between AI Agents," Future Internet, MDPI, vol. 16(8), pages 1-13, August.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:8:p:274-:d:1447726
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    References listed on IDEAS

    as
    1. Sikorski, Janusz J. & Haughton, Joy & Kraft, Markus, 2017. "Blockchain technology in the chemical industry: Machine-to-machine electricity market," Applied Energy, Elsevier, vol. 195(C), pages 234-246.
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