Monte Carlo Methods for the Game Kingdomino

TL;DR

This paper evaluates various Monte Carlo-based algorithms for playing Kingdomino, revealing that Monte Carlo Evaluation outperforms Monte Carlo Tree Search in this context, using a cloud-native multi-language framework.

Contribution

It introduces and compares Monte Carlo evaluation and tree search strategies for Kingdomino, highlighting the effectiveness of MCE over MCTS in this game.

Findings

MCE outperforms MCTS in Kingdomino.

A cloud-native multi-language architecture is effective for AI agents.

Parameter tuning improves strategy performance under time constraints.

Abstract

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.