Machine Learning and Games (MALAGA): Open Directions in Applying Machine Learning to Games

Computer games sales are three time larger than industry software sales, and on par with Hollywood box office sales. Modern computer games are often based on extremely complex simulations of the real world and constitute one of the very few real fields of application for artificial intelligence encountered in everyday live. Surprisingly, machine learning methods are not present in the vast majority of computer games. There have been a few recent and notable successes in turn-based two-player, discrete action space games such as Backgammon, Checkers, Chess and Poker. However, these successes are in stark contrast to the difficulties still encountered in the majority of computer games, which typically involve more than two agents choosing from a continuum of actions in complex artificial environments. Typical game AI is still largely built around fixed systems of rules that often result in implausible or predictable behaviour and poor user experience. The purpose of this workshop is to involve the NIPS community in the exciting challenges that games - ranging from traditional table top games to cutting-edge console and PC games - offer to machine learning.