SUSE AI Meets the Game Board by moio

Use tabletopgames.ai’s open source TAG and PyTAG frameworks to apply Statistical Forward Planning and Deep Reinforcement Learning to two board games of our own design. On an all-green, all-open source, all-AWS stack!

AI + Board Games

Board games have long been fertile ground for AI innovation, pushing the boundaries of capabilities such as strategy, adaptability, and real-time decision-making - from Deep Blue's chess mastery to AlphaZero’s domination of Go. Games aren’t just fun: they’re complex, dynamic problems that often mirror real-world challenges, making them interesting from an engineering perspective.

As avid board gamers, aspiring board game designers, and engineers with careers in open source infrastructure, we’re excited to dive into the latest AI techniques first-hand.

Our goal is to develop an all-open-source, all-green AWS-based stack powered by some serious hardware to drive our board game experiments forward!

Project Goals

1. Set Up the Stack:

   • Install and configure the TAG and PyTAG frameworks on SUSE Linux Enterprise Base Container Images.
   • Integrate with the SUSE AI stack for GPU-accelerated training on AWS.
   • Validate a sample GPU-accelerated PyTAG workload on SUSE AI.
   • Ensure the setup is entirely repeatable with Terraform and configuration scripts, documenting results along the way.

2. Design and Implement AI Agents:

   • Develop AI agents for the two board games, incorporating Statistical Forward Planning and Deep Reinforcement Learning techniques.
   • Fine-tune model parameters to optimize game-playing performance.
   • Document the advantages and limitations of each technique.

3. Test, Analyze, and Refine:

   • Conduct AI vs. AI and AI vs. human matches to evaluate agent strategies and performance.
   • Record insights, document learning outcomes, and refine models based on real-world gameplay.

Technical Stack

• Frameworks: TAG and PyTAG for AI agent development
• Platform: SUSE AI
• Tools: AWS for high-performance GPU acceleration

Why This Project Matters

This project not only deepens our understanding of AI techniques by doing but also showcases the power and flexibility of SUSE’s open-source infrastructure for supporting high-level AI projects. By building on an all-open-source stack, we aim to create a pathway for other developers and AI enthusiasts to explore, experiment, and deploy their own innovative projects within the open-source space.

Our Motivation

We believe hands-on experimentation is the best teacher.

Combining our engineering backgrounds with our passion for board games, we’ll explore AI in a way that’s both challenging and creatively rewarding. Our ultimate goal? To hack an AI agent that’s as strategic and adaptable as a real human opponent (if not better!) — and to leverage it to design even better games... for humans to play!

Digital art wallpapers for openSUSE Leap and Tumbleweed by lkocman

Description

We've enrolled set of new wallpapers to both Leap 16 and Tumbleweed as part of https://news.opensuse.org/2024/10/26/leap-tw-get-makeovers/

We've previewed digital art wallpapers which were not part of the initial drop. I'd like to spend time on hackweek to finialize my current Taipei (mountains) and Mauritius digital art wallpapers.

Goals

Finalize existing two digital art wallpapers for Leap and Tumbleweed https://github.com/openSUSE/branding/issues/155 Make them available as part of leap16 dir in https://github.com/openSUSE/wallpapers and update (This makes is available to Tumbleweed users as well). Update https://build.opensuse.org/package/show/X11:common:Factory/wallpapers-openSUSE-extra && Leap:16.0 && Factory.

Resources

https://github.com/openSUSE/branding/issues/155 The mauritius draft can be found in https://github.com/lkocman/geo-wallpapers