Towards a Celeste AI Framework: Agent-free Automated 2D Level Generation for Multidirectional Platformers

We present a procedural content generation (PCG) pipeline for Celeste, a complex 2D platformer with horizontal and vertical movement and with limited prior AI framework development. Our approach utilizes a Markov Chain-based model to capture the game's unique structural and gameplay elements, generating playable levels that adhere to Celeste's design principles. We implemented post-processing steps to enhance playability and strategically place game elements. Our evaluation metrics focused on playability and interestingness, with results that indicate success in replicating the desired gameplay experience for beginner players. The evaluation involved 12 players of different skill levels, providing insight into the effectiveness of our generated content. Although some limitations were observed, such as occasional lack of creativity and difficulty in controlling challenge levels, our pipeline demonstrates promise as a foundation for a Celeste AI framework. This study contributes to the broader field of PCG for complex platformers and opens avenues for level generation in which agent-based evaluation is not feasible.