Papers for

player behavior analysts

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

BehaviorFormer identifies distinct player actions in Minecraft gameplay

BehaviorFormer: Discovering Behavioral Patterns via Clusterable Next-Token Prediction

Abstract: Discovering behavioral patterns in open-ended games is challenging due to the lack of clear objectives or ground-truth annotations. In this work, we extend a next-token prediction Transformer with the deep clustering algorithm DCSS to learn meaningful representations with well-separated clusters. In a controlled Minecraft session with 26 players, the method discovers 23 distinct behavioral patterns that generalize across the studied players, further refining structures present in self-supervised representations into clearly separable clusters. We group the clusters into seven interpretable categories covering stationary, roaming, travel, mining, base work, logging, and farming behavior.

Mon 12 OctMachine LearningArtificial IntelligenceHuman-Computer Interaction

The gist

Finding patterns in how people play open-ended games is hard because there are no clear goals or labels. The authors combined a language prediction AI with a clustering method to group player actions into distinct categories. Using Minecraft data from 26 players, they found 23 different behavior patterns that represent common activities like mining and farming. These patterns help turn complex player actions into easy-to-understand groups.

Open → 2610.16322v1