Single Player Graph Building Game

Single-player graph-building games are computational frameworks used to explore graph theory conjectures and develop novel game-playing AI. Research focuses on designing efficient game environments, often employing reinforcement learning algorithms like deep Q-learning, to optimize strategies for maximizing or minimizing specific graph properties. These games provide a valuable tool for both theoretical advancements in graph theory and practical applications in areas like network robustness analysis and game AI development, offering a structured approach to complex problems. The development of general-purpose game engines and efficient feature extraction methods further enhances the applicability and scalability of this approach.