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

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Date
2014
Link to item file
https://hal.archives-ouvertes.fr/hal-02317151
Dewey
Intelligence artificielle
Sujet
Root Node; Group Node; Stochastic Game; Good Node; Classic Node
DOI
http://dx.doi.org/10.1007/978-3-319-14923-3_6
Conference name
3rd Workshop on Computer Games, CGW 2014, Held in Conjunction with the 21st European Conference on Artificial Intelligence, ECAI 2014
Conference date
08-2014
Conference city
Prague
Conference country
Czech Republic
Book title
ECAI Computer Games Workshop
Author
Cazenave, Tristan; Winands, Mark H. M.; Björnsson, Yngvi
Publisher
Springer
Pages number
163
ISBN
978-3-319-14923-3
Book URL
10.1007/978-3-319-14923-3
URI
https://basepub.dauphine.fr/handle/123456789/20900
Collections
  • LAMSADE : Publications
Metadata
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Author
Jouandeau, Nicolas
Cazenave, Tristan
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Type
Communication / Conférence
Item number of pages
78-89
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.

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