Monte-Carlo Tree Search is a powerful paradigm for deterministic perfect-information 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 lengths, with true or estimated wins. Results show that extending playout length over the real draw condition is beneficial to group nodes and to chance nodes. It also shows that using an evaluation function can reduce the number of draw games with group nodes and can be increased with chance nodes.
REF presented MCTS influence of various playout length, playout policy with heuristic board evaluation.