Small and large MCTS playouts applied to Chinese Dark Chess stochastic game

Abstract. Monte-Carlo tree search is a powerful paradigm for full infor-mation games. We present various changes applied to this algorithm to deal with the stochastic game Chinese Dark Chess. We experimented with group-nodes and chance-nodes using various configurations: with different playout policies, with different playout size, with true or evalu-ated wins. Results show that extending playout size over the real draw condition is beneficial to group-nodes and to chance-nodes. It also shows that using evaluation function can reduce the number of draw games with group-nodes and can be increased with chance-nodes.