Human-like agents for a Smartphone First Person Shooter game using Crowdsourced data

Abstract

The evolution of Smartphone devices with their powerful computing capabilities and their ever increasing number of sensors has recently introduced an unprecedented array of applications and games. The Smartphone users who are constantly moving and sensing are able to provide large amounts of opportunistic/ participatory data that can contribute to complex and novel problem solving, unfolding in this way the full potential of crowdsourcing. Crowdsourced data can therefore be utilized for optimally modeling human-like behavior and improving the realizablity of AI gaming. In this study, we have developed an Augmented Reality First Person Shooter game, coined AR Shooter, that allows the crowd to constantly contribute their game play along with various spatio-temporal information. The crowdsourced data are used for modeling the human player's behavior with Artificial Neural Networks. The resulting models are utilized back to the game's environment through AI agents making it more realistic and challenging. Our experimental studies have shown that our AI agents are quite competitive, while being very difficult to distinguish from human players. © IFIP International Federation for Information Processing 2013.