Label-Free Subjective Player Experience Modelling via Let's Play Videos

TL;DR

This paper introduces a new method for modeling player experience directly from gameplay videos, reducing the need for labor-intensive data collection, and demonstrates its effectiveness through a study on Angry Birds.

Contribution

It presents a novel, video-based approach to Player Experience Modelling that correlates well with self-reported and sensor-based affect measures.

Findings

Strong correlation with self-reported affect

Effective prediction of player affect from videos

Potential to simplify PEM development

Abstract

Player Experience Modelling (PEM) is the study of AI techniques applied to modelling a player's experience within a video game. PEM development can be labour-intensive, requiring expert hand-authoring or specialized data collection. In this work, we propose a novel PEM development approach, approximating player experience from gameplay video. We evaluate this approach predicting affect in the game Angry Birds via a human subject study. We validate that our PEM can strongly correlate with self-reported and sensor measures of affect, demonstrating the potential of this approach.