Monte Carlo Methods for the Game Kingdomino
This work addresses game-playing strategies for Kingdomino, an incremental improvement focusing on a specific domain with limited player interaction.
The paper tackled the problem of developing effective strategies for the game Kingdomino, finding that Monte Carlo Evaluation (MCE) surprisingly outperformed Monte Carlo Tree Search (MCTS) in this context, with specific parameter optimizations for time constraints.
Kingdomino is introduced as an interesting game for studying game playing: the game is multiplayer (4 independent players per game); it has a limited game depth (13 moves per player); and it has limited but not insignificant interaction among players. Several strategies based on locally greedy players, Monte Carlo Evaluation (MCE), and Monte Carlo Tree Search (MCTS) are presented with variants. We examine a variation of UCT called progressive win bias and a playout policy (Player-greedy) focused on selecting good moves for the player. A thorough evaluation is done showing how the strategies perform and how to choose parameters given specific time constraints. The evaluation shows that surprisingly MCE is stronger than MCTS for a game like Kingdomino. All experiments use a cloud-native design, with a game server in a Docker container, and agents communicating using a REST-style JSON protocol. This enables a multi-language approach to separating the game state, the strategy implementations, and the coordination layer.