Player Breeding System

Overview

The Player Breeding System is an advanced hybrid AI-driven platform designed to dynamically analyze player behavior, performance, and financial data to optimize player progression and engagement in online games. It leverages a combination of Rust-based core logic and Python microservices to collect data, tune game probabilities, and generate personalized quests using state-of-the-art AI models, including integration with the Together AI API.

Features

• Real-time player data collection from game clients (stats, achievements, financial, behavior).
• AI agents specialized by game genre to dynamically adjust drop rates, loot quality, and win probabilities.
• Personalized quest generation using DeepSeek-R1 AI model.
• Behavior tracking and clustering for adaptive quest and game design.
• Modular architecture with Rust core and Python microservices.
• MariaDB backend for centralized data storage.
• Configurable via TOML files.
• Dockerized deployment for easy setup and scalability.

Architecture Overview

• Rust Core: Implements game type detection (Blade AI), data orchestration, and AI agent logic for probability tuning.
• Python Microservices: Handle data ingestion, AI model integration, quest generation (Ioana AI), behavior analysis (Mao AI), and communication with Together AI API.
• MariaDB: Central database storing player stats, achievements, financial data, and behavior logs.
• Together AI API: Provides advanced AI capabilities for quest generation and dynamic adjustments.

Technology Stack

• Programming Languages: Rust, Python
• Database: MariaDB (accessed asynchronously via asyncmy)
• Web Framework: FastAPI (Python microservices)
• AI/ML Libraries: scikit-learn, PyTorch, LangChain, OpenAI API
• Configuration: TOML
• Containerization: Docker, Docker Compose

Setup and Installation

Prerequisites

• Docker and Docker Compose installed
• Rust toolchain installed (for building Rust components)
• Python 3.8+ installed (for running microservices locally)
• MariaDB server (can be run via Docker)

Installation Steps

1. Clone the repository:

   git clone <repository-url>
   cd Player-Breeding-System

2. Configure the database connection and AI parameters in src/config.toml.

3. Install Python dependencies:

   pip install -r requirements.txt

4. Initialize the database schema using migration scripts in the migrations/ directory.

5. Build Rust components:

   cargo build --release

6. Run the system using Docker Compose:

   docker-compose up --build

Alternatively, run Python microservices and Rust binaries separately for development.

Configuration

All configurable parameters are located in src/config.toml, including:

• Database connection URL
• AI agent thresholds and tuning parameters
• Together AI API key and model settings
• Behavior tracking and quest generation options

AI Agents Description

• Blade AI: Detects game type and routes data to appropriate genre agents.
• Claire, Earnest, Sophie Agents: Genre-specific agents that adjust game probabilities based on player data.
• Mao AI: Tracks player behavior and clusters data for adaptive quest design.
• Ioana AI: Generates personalized quests using the DeepSeek-R1 model.
• Together AI Integration: Provides advanced AI completions and model inference.

Roadmap and Next Steps

Refer to NEXT_STEPS.md for detailed plans including:

• Data collection API development
• Inter-agent communication protocols
• AI model training and evaluation
• Testing, monitoring, and logging enhancements
• Ethical compliance and privacy considerations
• Documentation and deployment improvements

Contributing

Contributions are welcome! Please fork the repository and submit pull requests for bug fixes, features, or improvements. Ensure code quality and include tests where applicable.

License

See the LICENSE file for license information.

References

• Configuration examples and API references are available in the references/ directory.
• Logs are stored in player_breeding_system_rCURRENT.log and rotated logs in player_breeding_system_r00000.log.
• Migration scripts for database schema are in the migrations/ folder.

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This README provides a comprehensive overview and guide to the Player Breeding System project for developers and contributors.

Workflow Visualization

flowchart TD
    GameClient("Game Client")
    BladeAI("Blade AI (Game Type Detection)")
    GenreAgents("Claire, Earnest, Sophie Agents (Genre-specific AI)")
    DataCollection("Data Collection Microservices")
    Database("MariaDB Database")
    MaoAI("Mao AI (Behavior Tracking)")
    IoanaAI("Ioana AI (Quest Generation)")
    TogetherAI("Together AI API")

    GameClient -->|Player Data| DataCollection
    DataCollection --> Database
    Database --> BladeAI
    BladeAI --> GenreAgents
    GenreAgents --> Database
    Database --> MaoAI
    MaoAI --> Database
    Database --> IoanaAI
    IoanaAI --> TogetherAI
    IoanaAI --> GameClient

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Developer 👨‍💻

Developed by Blackbeard | Ten Titanics | GitHub

© 2023-2024 Blackbeard. All rights reserved.