Compiling Strategic Games with Complete Information into Stochastic CSPs

Among the languages used for representing goals, actions and their consequences on the world for decision making and planning, GDL (Game Description Language) has the ability to represent complex actions in potentially uncertain and competitive environments. The aim of this paper is to exploit stochastic constraint networks in order to provide compact representations of strategic games, and to identify optimal policies in those games with generic forward checking method. From this perspective, we develop a compiler allowing to translate games, described in GDL, into instances of the Stochastic Constraint Optimization Problem (SCSP). Our compiler is proved correct for the class GDL of games with complete information and oblivious environment. The interest of our approach is illustrated by solving several GDL games with a SCSP solver.