NDLI: General game playing with stochastic CSP

Content Provider	Springer Nature Link
Author	Koriche, Frédéric Lagrue, Sylvain Piette, Éric Tabary, Sébastien
Copyright Year	2015
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 µ SCSPs (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 µ SCSP in turn, together with the UCB (Upper Confidence Bound) policy for approximating the values of those strategies obtained by the last µ SCSP 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.
Starting Page	95
Ending Page	114
Page Count	20
File Format	PDF
ISSN	13837133
Journal	Constraints
Volume Number	21
Issue Number	1
e-ISSN	15729354
Language	English
Publisher	Springer US
Publisher Date	2015-08-26
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Stochastic constraint satisfaction problem Stochastic games Game description language General game playing Artificial Intelligence (incl. Robotics) Optimization Computing Methodologies Operation Research/Decision Theory
Content Type	Text
Resource Type	Article
Subject	Discrete Mathematics and Combinatorics Artificial Intelligence Computational Theory and Mathematics Software

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