Mining Controller Inputs to Understand Gameplay
Brian A. Smith, Shree K. Nayar · 2016
Today's game analytics systems are powered by event logs, which reveal information about what players are doing but offer little insight about the types of gameplay that games foster. Moreover, the concept of gameplay itself is difficult to define and quantify. In this paper, we show that analyzing players' controller inputs using probabilistic topic models allows game developers to describe the types of gameplay -- or action -- in games in a quantitative way. More specifically, developers can discover the types of action that a game fosters and the extent that each game level fosters each type of action, all in an unsupervised manner. They can use this information to verify that their levels feature the appropriate style of gameplay and to recommend levels with gameplay that is similar to levels that players like. We begin with latent Dirichlet allocation (LDA), the simplest topic model, then develop the player-gameplay action (PGA) model to make the same types of discoveries about gameplay in a way that is independent of each player's play style. We train a player recognition system on the PGA model's output to verify that its discoveries about gameplay are in fact independent of each player's play style. The system recognizes players with over 90% accuracy in about 20 seconds of playtime.