  The article presents the use of Monte Carlo Tree Search algorithms for the
card game Lord of the Rings. The main challenge was the complexity of the game
mechanics, in which each round consists of 5 decision stages and 2 random
stages. To test various decision-making algorithms, a game simulator has been
implemented. The research covered an agent based on expert rules, using flat
Monte-Carlo search, as well as complete MCTS-UCB. Moreover different playout
strategies has been compared. As a result of experiments, an optimal (assuming
a limited time) combination of algorithms were formulated. The developed MCTS
based method have demonstrated a advantage over agent with expert knowledge.
