cognitive/Things/Path_Network

Path Network Simulation

A simulation of Active Inference agents in a dynamic network topology, where agents adapt their heights in response to changing water levels and network influences.

Features

• Active Inference Agents: Each node implements continuous-time active inference
• Dynamic Network Topology: Network connections evolve based on agent performance
• Environmental Dynamics: Complex water level patterns through nested sinusoidal waves
• Real-time Visualization: Interactive visualization of network state and agent behavior
• Comprehensive Analysis: Detailed metrics and visualizations of simulation results

Requirements

System Dependencies

• Python 3.8+
• Qt5 or Tk (for visualization)
• Required system libraries (installed automatically by setup script)

Python Dependencies

All Python dependencies are listed in requirements.txt and will be installed automatically by the setup script.

Quick Start

The easiest way to run the simulation is using the provided setup script:

./run_simulation.sh

This script will:

1. Create a Python virtual environment
2. Install required system dependencies
3. Install Python dependencies
4. Run the simulation
5. Save results to the output directory

Manual Setup

If you prefer to set up manually:

1. Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the simulation:

   python example.py
   

Output

The simulation creates a timestamped output directory containing:

• simulation.log: Detailed log of the simulation run
• initial_state.png: Network state visualization after initial period
• final_state.png: Network state visualization after perturbation
• water_level_distribution.png: Distribution of water levels
• agent_height_variations.png: Time series of agent heights
• water_levels.npy: Raw water level data
• agent_heights.npy: Raw agent height data

Visualization

The simulation provides real-time visualization of:

• Network topology with node colors representing heights
• Current agent heights compared to water level
• Historical evolution of heights and water levels

Configuration

Key parameters can be adjusted in example.py:

• Network parameters (number of nodes, connectivity, etc.)
• Wave components (frequencies, amplitudes, phases)
• Simulation duration and visualization intervals
• Perturbation parameters

Troubleshooting

Visualization Issues

If you encounter visualization problems:

1. The script will automatically try different backends (Qt5 → Tk → Agg)
2. Check if Qt5 or Tk is properly installed
3. Look for error messages in the simulation log

System Dependencies

If system dependencies fail to install:

1. Check your system's package manager
2. Install required packages manually:
   • For Ubuntu/Debian: sudo apt-get install python3-tk python3-qt5 libxcb-cursor0
   • For Fedora: sudo dnf install python3-tkinter python3-qt5 xcb-util-cursor
   • For Arch: sudo pacman -S python-tk qt5-base xcb-util-cursor

Contributing

Contributions are welcome! Please feel free to submit issues and pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.