Reimagining Gameplay: A NEAT Approach to Evolving AI in Player vs. AI Dynamics

Abstract: This paper examines the usage of NeuroEvolution of Augmenting Topologies (NEAT) to design an AI-driven dynamic game environment inspired by Flappy Bird mechanics, with a theme from the Naruto anime. It proposes a Player vs. AI competitive framework wherein AI agents represented asNarutoclonesevolveacrosssuccessivegenerationsto face increasingly difficult challenges of gameplay. Using NEAT, each clone starts with a unique neural network that is improved based on fitness criteria such as navigating obstacles and survival time. The game combines dynamic difficulty scaling, requiring the player to outlast evolving AI clones, with visually interesting mechanics such as changing backgrounds and responsive gameplay. Configurations forNEATinvolvedtanhactivation functions,controlled mutation rates, and optimizations to ensure efficient adaptation and robust AI performance. Within more than 10 generations, AI agents showed a 150% improvement in survival metrics, clearly showing the effectiveness of NEAT in evolving neural networks for real-time applications. This study also identifies limitations, such as NEAT's computational overhead and reliance on simplistic inputs, and proposes future directions to enhance AI adaptability and scalability. The findings emphasize NEAT's potential for dynamic gaming, robotics, and other domains requiring real-time AI evolution. This research not only advancesthe application of NEAT in interactive systems but also contributes to the broader discourse on adaptive AI in competitive environments.