Generating Interpretable Play-style Descriptions through Deep Unsupervised Clustering of Trajectories

Abstract

In any game, play style is a concept that describes the technique and strategy employed by a player to achieve a goal. Identifying a player’s style is desirable as it can enlighten players on which approaches work better or worse in different scenarios and inform developers of the value of design decisions. In previous work, we demonstrated an unsupervised LSTM-autoencoder clustering approach for play-style identification capable of handling multi-dimensional variable length player trajectories. The efficacy of our model was demonstrated on both complete and partial trajectories in both a simulated and natural environment. Lastly, through state frequency analysis the properties of each of the play styles were identified and compared. This work expands on this approach by demonstrating a process by which we utilise temporal information to identify the decision boundaries related to particular clusters. Additionally, we demonstrate further robustness by applying the same techniques to MiniDungeons, another popular domain for player modelling research. Finally, we also propose approaches for determining mean play-style examples suitable for describing general play-style behaviours and for determining the correct number of represented play-styles.

Branden Ingram

Lecturer

I am primarily interested in AI for games.

Richard Klein

PRIME Lab Director

I am an Associate Professor in the School of Computer Science and Applied Mathematics at the University of the Witwatersrand in Johannesburg, and a co-PI of the PRIME lab.

Benjamin Rosman

Lab Director

I am a Professor in the School of Computer Science and Applied Mathematics at the University of the Witwatersrand in Johannesburg. I work in robotics, artificial intelligence, decision theory and machine learning.