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.