Game Solving

Game solving research focuses on developing algorithms and models capable of finding optimal strategies in various game environments, ranging from simple board games to complex video games and even abstract mathematical games. Current research emphasizes improving the efficiency and robustness of existing methods like reinforcement learning and game-theoretic approaches, including the application of Shapley values and their extensions for explainability and the use of neural networks for handling imperfect information. These advancements have implications for artificial intelligence, particularly in areas like explainable AI and the development of more sophisticated game-playing agents, as well as for understanding complex systems through game-theoretic modeling.