Constraints

, Volume 21, Issue 1, pp 95–114 | Cite as

General game playing with stochastic CSP

  • Frédéric Koriche
  • Sylvain Lagrue
  • Éric Piette
  • Sébastien Tabary
Article

Abstract

The challenge of General Game Playing (GGP) is to devise game playing programs that take as input the rules of any strategic game, described in the Game Description Language (GDL), and that effectively play without human intervention. The aim of this paper is to address the GGP challenge by casting GDL games (potentially with chance events) into the Stochastic Constraint Satisfaction Problem (SCSP). The stochastic constraint network of a game is decomposed into a sequence of µSCSPs (also know as one-stage SCSP), each associated with a game round. Winning strategies are searched by coupling the MAC (Maintaining Arc Consistency) algorithm, used to solve each µSCSP in turn, together with the UCB (Upper Confidence Bound) policy for approximating the values of those strategies obtained by the last µSCSP in the sequence. Extensive experiments conducted on various GDL games with different deliberation times per round, demonstrate that the MAC-UCB algorithm significantly outperforms the state-of-the-art UCT (Upper Confidence bounds for Trees) algorithm.

Keywords

Stochastic constraint satisfaction problem Stochastic games Game description language General game playing 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Frédéric Koriche
    • 1
  • Sylvain Lagrue
    • 1
  • Éric Piette
    • 1
  • Sébastien Tabary
    • 1
  1. 1.Université Lille-Nord de France CRIL- CNRS UMR 8188LensFrance

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