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
Type
Communication / ConférenceExternal document link
https://hal.archives-ouvertes.fr/hal-02317151Date
2014Conference title
3rd Workshop on Computer Games, CGW 2014, Held in Conjunction with the 21st European Conference on Artificial Intelligence, ECAI 2014Conference date
2014-08Conference city
PragueConference country
Czech RepublicBook title
ECAI Computer Games WorkshopBook author
Cazenave, Tristan; Winands, Mark H. M.; Björnsson, YngviPublisher
Springer
ISBN
978-3-319-14923-3
Number of pages
163Pages
78-89
Publication identifier
Metadata
Show full item recordAuthor(s)
Jouandeau, Nicolas
Cazenave, Tristan
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 / Keywords
Root Node; Group Node; Stochastic Game; Good Node; Classic NodeRelated items
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