PreGLAM: A Predictive, Gameplay-based Layered Affect Model

Abstract

We present the Predictive Gameplay-based Layered Affect Model (PreGLAM), an affective game spectator model that flexibly integrates into a game design process. PreGLAM combines elements of real-time Player Experience Models, and Affective Non-Player-Character models to output real-time estimated values for a spectator's valence, arousal, and tension during gameplay. Because tension is related to prospective events, PreGLAM attempts to predict future gameplay events. We implement and evaluate PreGLAM in a custom game <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Galactic Defense</i> , which we also describe. PreGLAM significantly outperforms a random walk time series in how accurately it matches ground-truth annotations, and has comparable accuracy to state of the art affect models.