Monte Carlo Methods for the Game Kingdomino
  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.
