Collaborative Intelligence Component

Official implementation of the Best Paper Award-winning framework at PRO-VE 2025.

The Collaborative Intelligence Component is a web-based implementation of an Agentic Framework for Industry 5.0. It demonstrates a novel approach to Human-in-the-Loop (HITL) decision-making at the Edge, integrating Large Language Models (LLMs) to automate data curation and enable seamless, one-click model recalibration.

📄 Citation

If you use this code or framework in your research, please cite the following paper:

@inproceedings{martinez2025agentic,
  title={An Agentic Framework for Rapid Deployment of Edge AI Solutions in Industry 5.0},
  author={Martinez-Gil, Jorge and Pichler, Mario and Bountouni, Nefeli and Koussouris, Sotiris and Barreiro, Marielena M{\'a}rquez and Gusmeroli, Sergio},
  booktitle={Working Conference on Virtual Enterprises},
  pages={55--68},
  year={2025},
  organization={Springer}
}

🎥 Video Demonstration

Watch the system in action, featuring real-time MQTT data processing and ChatGPT 4o integration.

https://www.youtube.com/watch?v=AR8F8U-QXhM

🔬 Scientific Contribution & Capabilities

This repository implements the "Collaborative Intelligence" layer described in the associated paper. It bridges the gap between static Edge AI models and dynamic industrial environments through:

1. Human-Machine Collaboration (Industry 5.0)

Unlike traditional "black box" deployments, this tool provides a Dynamic Validation Interface where human experts can review, correct, and curate AI predictions in real-time.

2. Agentic Reasoning & XAI

The system integrates Generative AI (ChatGPT 4o) to act as an intelligent agent. It not only labels data but provides Explainable AI (XAI) reasoning for why a prediction was flagged as "OK" or "Non-OK," reducing the cognitive load on human operators.

3. Rapid Edge Recalibration

The component features an automated feedback loop. Validated data (from humans or the GenAI agent) is used to recalibrate the Edge AI model with a single click, allowing the system to adapt to data drift without offline retraining cycles.

4. Synthetic Data Augmentation

To address the data scarcity common in industrial settings, the system leverages LLMs to generate additional training samples based on expert-curated positive and negative examples.

⚙️ System Architecture

The solution follows a modular architecture designed for Edge deployment:

• Frontend: Lightweight HTML/CSS/JS interface for low-latency interaction.

• Communication Layer: MQTT-based telemetry (compatible with HiveMQ, Mosquitto) for real-time sensor streams.

• Intelligence Layer:

  • Edge Model: Handles immediate inference.

  • Cloud Agent: Connects to GenAI APIs (Chatbase/OpenAI) for higher-order reasoning.

• Visualization: Plotly.js for dynamic, real-time performance monitoring.

🚀 Usage Guide

Prerequisites

• Modern Web Browser (Chrome, Firefox, Edge).

• Active MQTT Broker (e.g., HiveMQ).

• Configuration file (see below).

Quick Start

1. Launch: Open index.html in your browser.

2. Configure: Click "Load Config" and upload your .json mapping file.

3. Connect: Click "Connect to Broker" to start the real-time MQTT stream.

4. Collaborate:

   • Monitor incoming predictions in the Dynamic Table.

   • Use the "GenAI" button to auto-generate reasoning for anomalies.

   • Manually correct targets if necessary.

5. Improve: Click "Recalibrate Model" to update the edge model logic instantly.

Configuration (config.json)

Strictly define your MQTT topics and feature vectors:

JSON

{
    "brokerURL": "wss://[broker.hivemq.com:8000/mqtt](https://broker.hivemq.com:8000/mqtt)",
    "inputTopic": "industry/edge/input",
    "outputTopic": "industry/edge/output",
    "inputs": [
        {"name": "Vibration_Sensor_X"},
        {"name": "Temperature_C"},
        {"name": "Pressure_PSI"}
    ]
}

🛠 Customization

• Styling: Fully customizable CSS for white-labeling.

• Model Backend: The JavaScript logic is modular; predict() functions can be swapped for TensorFlow.js or ONNX runtimes.

🤝 Acknowledgment

This work is supported by the AI REDGIO 5.0 project: "Regions and (E)DIHs alliance for AI-at-the-Edge adoption by European Industry 5.0 Manufacturing SMEs" under EU Grant Agreement No. 101092069.