Exploiting Game Decompositions in Monte Carlo Tree Search

International audienceIn this paper, we propose a variation of the MCTS framework to perform a search in several trees to exploit game decompositions. Our Multiple Tree MCTS (MT-MCTS) approach builds simultaneously multiple MCTS trees corresponding to the different sub-games and allows , like MCTS algorithms, to evaluate moves while playing. We apply MT-MCTS on decomposed games in the General Game Playing framework. We present encouraging results on single player games showing that this approach is promising and opens new avenues for further research in the domain of decomposition exploitation. Complex compound games are solved from 2 times faster (Incredible) up to 25 times faster (Nonogram)

Statistical GGP Game Decomposition

International audienceThis paper presents a statistical approach for the decomposition of games in the General Game Playing framework. General game players can drastically decrease game search cost if they hold a decomposed version of the game. Previous works on decomposition rely on syn-tactical structures, which can be missing from the game description, or on the disjunctive normal form of the rules, which is very costly to compute. We offer an approach to decompose single or multi-player games which can handle the different classes of compound games described in Game Description Language (parallel games, serial games, multiple games). Our method is based on a statistical analysis of relations between actions and fluents. We tested our program on 597 games. Given a timeout of 1 hour and few playouts (1k), our method successfully provides an expert-like decomposition for 521 of them. With a 1 minute timeout and 5k playouts, it provides a decomposition for 434 of them

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