Monte Carlo Tree Search for the Hide-and-Seek Game Scotland Yard

This paper describes how Monte Carlo tree search (MCTS) can be applied to the hide-and-seek game Scotland Yard. This game is essentially a two-player game in which the players are moving on a graph-based map. First, we discuss how determinization is applied to handle the imperfect information in the game. We show how using determinization in a single tree performs better than using separate trees for each determinization. We also propose a new technique, called location categorization, that biases the possible locations of the hider. The experimental results reveal that location categorization is a robust technique, and significantly increases the performance of the seekers. Next, we describe how to handle the coalition of the seekers by using coalition reduction. This technique balances each seeker´s participation in the coalition. Coalition reduction improves the performance of the seekers significantly. Furthermore, we explain how domain knowledge is incorporated by applying ε-greedy playouts and move filtering. Finally, we compare the MCTS players to minimax-based players, and we test the performance of our MCTS player against a commercial Scotland Yard program on the Nintendo DS. Based on the results, we may conclude that the MCTS-based hider and seekers play at a strong level.