STAR-EX AI <AWS>

A new idle gameplay in STAR-EX world, collecting resources from across blockchain, then craft them to advance or sell them into market.

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STAR-EX: Expedition Game Demo (AWS Hackathon)

Inspiration

STAR-EX was born from a vision to create the most dynamic and immersive blockchain game universe. By combining cross-chain, cross-gameplay, cross-dimension, cross-platform, AI-driven systems, and strategic competition, we aim to redefine the GameFi experience. The addition of the Expedition Game Demo for the AWS Hackathon showcases our commitment to innovation. With AWS-Q helping the development of this project and other AI services, we've streamlined development and unlocked new possibilities in game design.

For those who remember, STAR-EX is designed to feature three core gameplay modes, each utilizing the same shared resources to create a seamless, interconnected experience:

  1. EXCEED RACE – A competitive, high-speed race across various sectors of the galaxy.
  2. EXPLORATION – Intense arcade, resource-gathering mode that allows players to discover new planets and gather valuable materials.
  3. EXPEDITION – A strategic idle, where players will embark on tactical missions, manage resources, and expand their influence across the STAR-EX universe.

I have developed the exploration gameplay from other hackathon and improving the lore + community to make it more cooler

Learn more about the development for EXPEDITION process in our blog post.

updated FULL FEEDBACK described in the blog

What it does

The Expedition Game Demo introduces:

  • Idle Gameplay Mode: Players strategically gather resources and explore new planets without requiring constant engagement.
  • Shared Resource Economy: Players compete for resources that are also used in other gameplay modes like 3D Exploration and future team-based Exceed.
  • AI Integration: Game balance and economy are managed by AI agents, ensuring fairness and dynamic adjustments.
  • Cross-Blockchain Competition: Players represent their blockchain communities in a battle for zone-territory dominance.

Links:

How we built it

We utilized a robust stack of AWS services and AI tools:

  • Backend: Built with AWS DynamoDB, AWS Lambda, and AWS RDS for real-time multiplayer (on-hold).
  • Frontend: Developed and deployed with AWS Amplify and AWS S3.
  • AI Development:
    • AWS Bedrock: Created in-game AI-agent for AI-assistants and AI-navigators.
    • AWS-Q: Daily tools to help development workflow, improve developer experience and productivity.
  • AI Services in Progress: Leveraging AWS Nova and AWS Rekognition for intelligent game systems.

AWS Bedrock Overview

Challenges we ran into

  • Amazon Learning Complexity: Because there's so many product service, It was overwhelm at the beginning but once I know each product capability, I know what to do, and AWS-Q also can help me to quickstart easily.
  • Cross-Blockchain Gameplay: Ensuring seamless interaction and competition between blockchain communities posed significant technical challenges.
  • Dynamic Balance: Im going to design AI systems with sagemaker and train the model to manage resource allocation dynamically while maintaining player engagement and fairness.

Accomplishments that we're proud of

  • AMAZON-Q, Im so proud learning this. Freaking amazing tool.
  • Successfully integrated an idle gameplay mode into the STAR-EX universe.
  • Created a robust, AI-driven economy system to sustain the game's dynamic environment.
  • Leveraged AWS services to streamline development and deployment, allowing us to focus on innovation.

What we learned

  • The power of AWS-Q in enhancing development workflows and improving the developer experience.
  • The importance of AI-driven systems in managing complex economies and gameplay dynamics.
  • Strategies for optimizing idle gameplay to increase player engagement while maintaining simplicity.

What's next for STAR-EX AI

  1. Advanced AI Systems:
    • Learning develop AI governance utilising AWS Nova and AWS Kendra for Retrieval-Augmented Generation (RAG).
    • Use AWS SageMaker Canvas for rapid AI prototyping, and train the model easily (I CANT WAIT till this happen, Im so happy learning a lot of cool things now, especially with my new friend AWS-Q).
  2. Player Data Insights:
    • Leverage AWS Forecast to analyze player data for predictive balancing and economic adjustments, with their NFT-asset.
  3. Custom AI Models:
    • Train proprietary AI systems to manage in-game governance and world updates.
  4. Future Game Modes:
    • Launch the Exceed Mode, introducing team-based gameplay.
    • Develop new gameplay mechanics that further integrate AI for immersive experiences.
  5. 3D development: I havent try to develop the new 3d mechanic with AMAZON-Q but im gonna start it as soon as I finished another task.

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