MCTS Based Agents for Multistage Single-Player Card Game
This work addresses decision-making in a complex multistage single-player card game, but it is incremental as it applies existing MCTS methods to a new game domain.
The researchers tackled the complexity of the Lord of the Rings card game, which involves multiple decision and random stages, by developing and testing Monte Carlo Tree Search (MCTS) based agents, and found that the MCTS method demonstrated an advantage over an expert rule-based agent.
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.