AUSA - Adaptive Universal Simulation Agent

Introduction

AUSA (Adaptive Universal Simulation Agent) is a pioneering project that explores the adaptability of AI-powered humanoids in extraterrestrial environments. The project utilizes advanced machine learning algorithms to train a humanoid model that can predict its performance capabilities and respond effectively to various planetary conditions.

Project Overview

The core of AUSA involves simulating and analyzing how AI-humanoid robots can rapidly adapt to diverse and challenging environmental circumstances, particularly in space. By integrating essential Earth environmental data, the model is designed to transition from terrestrial to extraterrestrial conditions gradually, focusing on critical factors like temperature, radiation, and atmospheric composition.

Features

• Environmental Adaptability: The AI-humanoid model is trained to adapt to varying environmental conditions, including temperature fluctuations, radiation levels, and atmospheric changes.
• Human-Like Characteristics: The humanoid's physical, behavioral, and cognitive characteristics are modeled using advanced algorithms to ensure realistic interactions in simulated environments.
• Multi-Model Approach: Various neural network models, including LSTM, GRU, RNN, and CNN, are employed to optimize the humanoid's adaptability and performance in extraterrestrial settings.

Models Used

• LSTM (Long Short-Term Memory)
• GRU (Gated Recurrent Unit)
• RCNN (Recurrent Convolutional Neural Network)
• CNN (Convolutional Neural Network)
• RNN (Recurrent Neural Network)

Data Set

The dataset used in AUSA is derived from both imaginary and real-world parameters, tailored to simulate extraterrestrial conditions. Key parameters include:

• Initial Parameters:

  • Skin Tone (Fitzpatrick Scale)
  • Continent
  • Climate
  • Radiation Level

• Final Parameters:

  • Duration
  • Continent
  • Climate
  • Radiation Level
  • Impact

Results

The following are the results obtained from various models after training:

Model	Epochs	Batch Size	Units	Test Loss
LSTM	1000	32	50	0.0981
Bi-LSTM	300	32	50	0.1130
GRU	200	32	50	0.0939
RNN	500	32	300	0.0460
Linear Regression	N/A	N/A	N/A	0.4897
Decision Tree	N/A	N/A	N/A	0.0150
Random Forest	N/A	N/A	N/A	0.0130
XG Boost	N/A	N/A	N/A	0.0064

Result Charts

You can find the detailed charts and visualizations of the results in the results directory of this repository. These charts provide a visual representation of the model performances and their respective accuracies.

Conclusion

The AUSA project provides a systematic investigation into the adaptability of AI-humanoids in extraterrestrial settings. Through rigorous training and simulation, the project offers valuable insights into the challenges and opportunities of deploying adaptive AI systems in space exploration.

Contributors

• Syed Kumail Haider - Idea Contribution and Model Development
  • LinkedIn: Syed Kumail Haider
• Uswa Mariam - Environmental Adaptation Strategies and Visionary Contributions
  • LinkedIn: Uswa Mariam

License

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