Game Playing Agent

Game-playing agents are artificial intelligence systems designed to play games, serving as a testbed for developing advanced AI capabilities like planning, reasoning, and learning. Current research emphasizes creating agents adaptable to diverse games and board sizes, often employing transformer-based architectures and reinforcement learning algorithms like AlphaZero, along with techniques such as self-play and imitation learning to improve performance and generalization. This field is significant because it pushes the boundaries of AI, yielding insights applicable to broader problems in decision-making, optimization, and human-computer interaction, as evidenced by applications in areas like psychological assessment and combat simulation.