Human Player

Research on the "human player" focuses on understanding and replicating human behavior in game contexts, aiming to improve AI agents and gain insights into human decision-making. Current efforts involve developing deep learning architectures, such as structurally variable networks, to mimic human play styles across diverse games, from board games to first-person shooters, and employing techniques like imitation learning from human demonstrations or game transcripts. This research contributes to advancements in AI, particularly in explainable AI and human-computer interaction, and has implications for game development, security against cheating, and the design of more human-centered AI systems.