Game Representation

Game representation research focuses on efficiently and effectively encoding game information for use in artificial intelligence, particularly in areas like game playing, procedural content generation, and player modeling. Current efforts explore diverse approaches, including embedding techniques that leverage word embeddings to capture thematic elements, self-supervised learning methods to extract informative representations directly from game visuals, and novel clustering algorithms applied to various data types (e.g., DNA sequences represented as images, game state data). These advancements aim to improve the performance and generalizability of AI agents across different games and enhance our understanding of game dynamics, with implications for both theoretical computer science and practical applications in game development and beyond.