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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.
Computational Intelligence and AI in Games, IEEE Transactions on (Volume:4 , Issue: 4 )
Date of Publication: Dec. 2012