WoodStook: A stochastic constraint-based general game players

WoodStook: A stochastic constraint-based general game players

Frédéric Koriche Sylvain Lagrue Éric Piette Sébastien Tabary 

Université Lille-Nord de France CRIL - CNRS UMR 8188 Artois, F-62307 Lens

Corresponding Author Email: 
koriche@cril.fr; lagrue@cril.fr; epiette@cril.fr; tabary@cril.fr
Page: 
281-310
|
DOI: 
https://doi.org/10.3166/RIA.31.281-310
Received: 
|
Accepted: 
|
Published: 
30 June 2017
| Citation

OPEN ACCESS

Abstract: 

This article describes WoodStock, the first general game player modeling each game from the General Game Playing (GGP) by a stochastic constraint network (SCSP). Each action played is decided by the resolution of this last one by the algorithm MAC-UCB. After the translation of an instance described in Game Description Language (GDL) in a network representative of the state of the game at any time, WoodStock solves each state by the maintening arc-consistency algorithm (MAC) iteratively guided by the bandit-based stochastic sampling (UCB) of the next states. Thanks to this algorithm, WoodStock is since march 2016, the leader of the GGP Tiltyard continuous tournament. Moreover, in its last version exploiting the game symmetries finding by the constraint symmetry detection, the search space associated with a game is significatively reduced. With that, WoodStock is now the GGP champion after its victory at the International General Game Playing Competition 2016 (IGGPC 2016).

Keywords: 

international general game playing competition (IGGPC), stochastic constraint satisfaction problem (SCSP), bandit-based stochastic sampling (UCB)

1. Introduction
2. General Game Playing (GGP)
3. Programmation par Contraintes Stochastiques (SCSP)
4. WoodStock
5. Résultats compétitifs
6. Optimisation
7. Conclusion
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