Optimal Player Information MCTS applied to Chinese Dark Chess

Abstract

Alpha-beta and Monte-Carlo Tree Search (MCTS) are two powerful paradigms useful in computer games. When considering imperfect information, the tree that represents the game has to deal with chance. When facing such games that presents increasing branching factor, MCTS may consider, as alpha-beta do, pruning to keep efficiency. We present a modified version of MCTS-Solver algorithm, called OPI-MCTS as Optimal Player Information MCTS, that adds game state information to exploit logical reasoning during backpropagation and that influences selection and expansion. OPI-MCTS is experimented in Chinese Dark Chess, which is a imperfect information game.OPI-MCTS is compared with classical MCTS.