Small and large MCTS playouts applied to Chinese Dark Chess stochastic game
Jouandeau, Nicolas; Cazenave, Tristan (2014), Small and large MCTS playouts applied to Chinese Dark Chess stochastic game, in Cazenave, Tristan; Winands, Mark H. M.; Björnsson, Yngvi, ECAI Computer Games Workshop, Springer, p. 78-89. 10.1007/978-3-319-14923-3_6
TypeCommunication / Conférence
External document linkhttps://hal.archives-ouvertes.fr/hal-02317151
Conference title3rd Workshop on Computer Games, CGW 2014, Held in Conjunction with the 21st European Conference on Artificial Intelligence, ECAI 2014
Conference countryCzech Republic
Book titleECAI Computer Games Workshop
Book authorCazenave, Tristan; Winands, Mark H. M.; Björnsson, Yngvi
Number of pages163
MetadataShow full item record
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)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.
Subjects / KeywordsRoot Node; Group Node; Stochastic Game; Good Node; Classic Node
Showing items related by title and author.