An intelligent Etimo Diamonds game bot powered by sophisticated greedy algorithms
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Genderuwo Bot is an advanced AI agent designed to dominate the Etimo Diamonds game using cutting-edge algorithmic strategies. The bot employs a Multi-Weighted Greedy Algorithm that combines intelligent decision-making with dynamic heuristics to maximize diamond collection efficiency.
- Dynamic Heuristic Greedy (DHG) approach for optimal move selection
- Multi-factor weighted decision system considering:
- Distance optimization
- Point value assessment
- Strategic resource utilization
- Advanced game mechanics integration including red buttons and teleporters
- Real-time adaptive strategy based on game state analysis
The heart of our intelligence lies in the Multi-Weighted algorithm, meticulously crafted and located in:
📁 game/logic/multi_weighted.py
Ensure you have the following tools installed on your system:
| Tool | Purpose | Download Link |
|---|---|---|
| Node.js | JavaScript runtime | nodejs.org |
| Python 3.x | Core language | python.org |
| Docker Desktop | Containerization | docker.com |
| Yarn | Package manager | Install via npm |
Follow these simple steps to get Genderuwo Bot up and running:
# Download the source code
git clone https://github.com/MarioSitepu/Tubes1_Sigma.git
# Go Into The Source Code
cd src# Install Python dependencies
pip install -r requirements.txt
# Install Yarn globally (if not already installed)
npm install --global yarn# Run Genderuwo Bot with custom configuration
python main.py \
--logic MultiWeighted \
--email=multi@email.com \
--name=Genderuwo \
--password=123456 \
--team etimo💡 Pro Tip: Customize the bot parameters according to your game strategy needs!
The Genderuwo Bot project has reached completion status with all core functionalities implemented and tested. The bot demonstrates consistent performance in diamond collection scenarios with optimized decision-making capabilities.
Our development roadmap includes several exciting enhancements:
- Algorithm processing speed improvements
- Code efficiency and memory optimization
- Resource utilization enhancements
- Enhanced obstacle avoidance mechanisms
- Basic A* pathfinding algorithm implementation
- Improved movement prediction systems
- Competitive intelligence analysis
- Enemy behavior prediction models
- Dynamic strategy switching capabilities
- Parameter fine-tuning for optimal performance
- Real-time performance monitoring
- Advanced debugging and logging systems
Special thanks to our talented development team who made this project possible:
| Developer | GitHub Profile | Contribution |
|---|---|---|
| Anselmus Herpin Hasugian | Algorithm Design & Implementation | |
| Mario Fransiskus Sitepu | @MarioSitepu | Core Logic & Optimization |
| Margareta Angela Manullang | Testing & Documentation |
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