Game Play

Game play research explores how humans and artificial intelligence agents make decisions and learn strategies within game environments, aiming to understand both optimal play and the cognitive processes involved. Current research focuses on enhancing algorithms like Monte Carlo Tree Search (MCTS) and integrating deep learning models, particularly Graph Neural Networks (GNNs), to improve agent performance and interpretability in diverse game types, from board games to real-time video games. These advancements have implications for artificial intelligence development, offering valuable insights into decision-making, learning, and the creation of more robust and explainable AI systems, as well as providing new benchmarks for evaluating large language models.