Incomplete Information Game

Incomplete information games, where players lack complete knowledge of the game state or other players' actions, are a significant area of research in artificial intelligence and game theory. Current research focuses on developing effective algorithms, such as variations of Counterfactual Regret Minimization (CFR) and tree search methods incorporating equilibrium approximations, to enable agents to successfully navigate these complex scenarios, particularly in games like poker and Mahjong. These advancements have implications for both theoretical understanding of multi-agent decision-making under uncertainty and practical applications in areas like cloud computing pricing strategies and the mitigation of misinformation spread online. The development of more human-like agents in these games also contributes to the broader field of AI.