We compare a “cheating” agent (with access to hidden information) to an agent using determinization with random deals. This paper studies the strengths and weaknesses of determinization coupled with MCTS on a game of imperfect information, the popular Chinese card game Dou Di Zhu. Monte-Carlo Tree Search (MCTS) is an AI technique that has recently proved successful in the domain of deterministic games of perfect information. The results show that the MCTS-based program plays stronger than this program.ĭeterminization is a technique for making decisions in games with stochasticity and/or imperfect information by sampling instances of the equivalent deterministic game of perfect information. Finally, we test the performance of our MCTS program against a commercial Scotland Yard program on the Nintendo DS. Furthermore, we explain how domain knowledge is incorporated by applying e-greedy playouts for the hider and the seekers and move filtering to improve the performance of the hider. Coalition Reduction improves the performance of the seekers significantly. This technique balances each seeker's participation in the coalition by letting them seek the hider more greedily. Next, we show how to handle the coalition of the seekers in Scotland Yard by using Coalition Reduction. The experimental results show that Location Categorization is a robust technique which significantly increases the performance of the seekers in Scotland Yard. We also propose a new technique, called Location Categorization, that biases the possible locations of the hider. We show how limiting the number of possible locations of the hider by using information about the hider's moves increases the performance of the seekers considerably. It is essentially a two-player game in which the players are moving on a graph-based map. This paper describes how Monte-Carlo Tree Search (MCTS) can be applied to play the hide-and-seek game Scotland Yard.
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