AI6 AI in Games: Strategies and Limitations

This academic text focuses on adversarial search and game theory within artificial intelligence, exploring how AI agents navigate environments where others actively work against them. It primarily discusses game-playing algorithms like minimax and alpha-beta pruning for deterministic, perfect-information games, detailing their mechanics and limitations. The document also addresses more complex scenarios, including stochastic games (involving chance elements like dice) and partially observable games (where information is hidden), introducing expectiminimax and Monte Carlo Tree Search (MCTS) as alternative strategies. Finally, it touches upon the integration of machine learning to enhance game AI, citing examples of AI surpassing human performance in various games like chess and Go.