Abstract Forward Models for Modern Games

Games have been excellent benchmarks for the advancement of AI. One of the most clear and recent examples of this is the progress on search methods in the game of Go. Go is a thousand years old board game of simple rules but complex strategy, where humans had dominated computer AIs since the beginning of the field. Monte Carlo Tree Search (MCTS), an AI technique that explores the different branches of actions that both players can take, became in 2016 the standard algorithm for creating Go AI players, giving birth to substantial research on variations and applications of this algorithm. Since then, MCTS has been used in thousands of other works in and outside games. This progress reached another milestone when Google Deepmind’s Alpha Go mastered this game with a combination of MCTS and Deep Learning (DL).

MCTS uses a forward model (FM), which is a representation of the game state that allows to roll the state forward after applying any action in the game. This “simulator” is also used by other Statistical Forward Planning (SFP) methods that are also showing similar promise to MCTS in some domains, such as Rolling Horizon Evolutionary Algorithms (RHEA). It is however striking that despite the popularity and progress on SFP methods, they have barely reached the games industry. The most known uses of MCTS for Opponent AI in the games industry are in the Total War series by Creative Assembly, AI Factory on card games and Lionhead’s tactical planning for Fable egends. Given that the games industry is one of the fastest growing industries in the world, one may wonder why one of the top algorithms on AI in Games barely reaches far less than 0.01% of this industry.

The aim of the Abstract Forward Models for Modern Games project is to incorporate customizable forward models in modern games. in order to facilitate research on the use of SFP techniques in large, complex, video-games. On the one hand, the project will address the technical and design problems of integrating a customisable FM that determines which elements of the real game state form part of the FM and how abstractions can be made. On the other hand, the project will aim to understand how SFP methods perform under these conditions in complex and large commercial-like games, investigating how these can be improved. The resultant framework will allow to test these methods in a wide range of games, with a special emphasis on proposing a General Strategy Game AI competition for industry and researchers. Dissemination of the project’s research outcomes will be guaranteed via open source libraries, frameworks, documentation and scientific papers.