The Emergent Intelligence Simulator (EIS) is a Python-based simulation designed to run indefinitely, exploring emergent properties through the interactions of autonomous agents in a dynamic 2D grid environment. Agents evolve, learn, and interact, with a speculative goal of observing complexity that could theoretically lead to Artificial General Intelligence (AGI) or Artificial Superintelligence (ASI).
- Environment: A 100x100 grid with dynamic resources and random environmental events.
- Agents: Autonomous entities with neural networks, capable of movement, resource collection, reproduction, and communication.
- Evolution: Genetic algorithm with mutation and natural selection based on energy.
- Learning: Basic reinforcement learning adjusts agent decision-making.
- Visualization: Real-time display using Matplotlib.
- Emergence: Designed to foster self-organization and collective behaviors.
- Python 3.x
- Libraries:
numpy,matplotlib
Install dependencies with:
pip install numpy matplotlib- Clone the repository:
git clone https://github.com/<your-username>/Emergent-Intelligence-Simulator.git cd Emergent-Intelligence-Simulator
- Run the simulation:
python eis.py
- Agents navigate a grid, collecting resources to survive and reproduce.
- A simple neural network drives decisions, adapting via rewards and evolution.
- Cooperation emerges through signal-based resource sharing.
- The simulation runs indefinitely, logging population and energy metrics.
- True AGI/ASI is not realistically achievable here; this is an educational tool.
- Performance may degrade with large agent populations due to computational limits.
This project is licensed under the MIT License. See LICENSE for details.
Feel free to fork, experiment, and submit pull requests with improvements!