Recent Advances in General Game Playing

The goal of General Game Playing (GGP) has been to develop computer programs that can perform well across various game types. It is natural for human game players to transfer knowledge from games they already know how to play to other similar games. GGP research attempts to design systems that work well across different game types, including unknown new games. In this review, we present a survey of recent advances (2011 to 2014) in GGP for both traditional games and video games. It is notable that research on GGP has been expanding into modern video games. Monte-Carlo Tree Search and its enhancements have been the most influential techniques in GGP for both research domains. Additionally, international competitions have become important events that promote and increase GGP research. Recently, a video GGP competition was launched. In this survey, we review recent progress in the most challenging research areas of Artificial Intelligence (AI) related to universal game playing

Instantiating General Games Using Prolog or Dependency Graphs

Abstract. This paper proposes two ways to instantiate general games specified in the game description language GDL to enhance exploration efficiencies of ex-isting players. One uses Prolog’s inference mechanism to find supersets of reach-able atoms and moves; the other one utilizes dependency graphs, a datastructure that can calculate the dependencies of the arguments of predicates by evaluating the various formulas from the game’s description.