Game Tree

Game trees are fundamental data structures representing the possible sequences of actions in games, crucial for analyzing strategic decision-making. Current research focuses on improving algorithms like Monte Carlo Tree Search (MCTS) and its integration with neural networks, as well as developing efficient methods for solving extensive-form games, particularly those with imperfect information and multiple players. These advancements are driving progress in areas such as automated theorem proving and AI for complex games like chess and Xiangqi, offering insights into game theory and potentially impacting the design of more robust and efficient AI systems.