Exploring uncertainty in games

Imperfect information games are an excellent example of decision making under uncertainty. In particular, some games have such an immense size and high degree of uncertainty that traditional algorithms and methods struggle to play them effectively. Monte Carlo Tree Search (MCTS) has brought significant improvements to the level of computer players in games such as Go, and it has been used to play imperfect information games as well, but there are certain games with particularly large trees and reduced information in which this class of algorithms can fail, especially in the presence of long matches, dynamic information and complex victory conditions.