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

2024/10/03 by Dave Goel, Athar Mahmoudi-Nejad, Goel, Dave +3
Computer Science · Economics, Econometrics and Finance · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Music and Audio Processing #Sports Analytics and Performance #Video Analysis and Summarization

paper · pdf · doi:10.48550/arxiv.2410.02967

openalex publication_date 2024/10/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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