Building Intelligent Games (rasmusrasmussen.com)

A game developer has successfully implemented transformer networks to dynamically adjust gameplay difficulty in a new tech demo called "Space Base Bomb Run" (SBBR). Unlike traditional methods of setting difficulty through hard-coded values or sliders, this innovative approach leverages real-time player data—such as movement, kills, and gameplay time—to enable the AI to modulate challenges on the fly. This method offers increased adaptability, allowing the game to respond to individual player performance without the need for extensive programming adjustments. The neural network underlying this AI game master is relatively small and easy to train, as it only requires input data such as player statistics and predefined output actions like enemy spawning or adjusting game pressure. The developer trained the model using over 1,000 labeled entries, demonstrating that such training, although tedious, is manageable. Following successful training, the model can be integrated into game engines, making it accessible for developers looking to enhance player experience through more responsive gameplay. This approach showcases the potential of using compact transformer models in gaming, paving the way for further advancements in AI-driven gameplay dynamics.