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

Nicolas Jouandeau (1) , Tristan Cazenave (2)

1 LIASD - Laboratoire d'Informatique Avancée de Saint-Denis
2 LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision

Monte-Carlo tree search is a powerful paradigm for full 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 size, with true or evaluated 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.

Domaines: Intelligence artificielle [cs.AI]

Nicolas Jouandeau, Tristan Cazenave. Small and large MCTS playouts applied to Chinese Dark Chess stochastic game. ECAI Computer Games Workshop, Aug 2014, Prague, Czech Republic. ⟨10.1007/978-3-319-14923-3_6⟩. ⟨hal-02317151⟩