Imperfect Information Game

Imperfect information games, where players lack complete knowledge of the game state, pose significant challenges for artificial intelligence, demanding strategies that account for uncertainty and hidden information. Current research focuses on developing efficient algorithms, such as counterfactual regret minimization (CFR) and its variants, policy gradient methods, and Monte Carlo tree search (MCTS) adaptations, often combined with neural networks for function approximation and opponent modeling. These advancements are improving AI performance in complex games like poker and Mahjong, contributing to a deeper understanding of multi-agent decision-making under uncertainty and potentially impacting fields like negotiation, cybersecurity, and strategic planning.