Bayesian clustering of player styles for multiplayer games

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Abstract

With game play data, empirical approaches to clustering are typically based solely on game outcomes, e.g. kills, deaths, and score for each player. In this paper, we investigate a method for clustering players based on how a player's choices relate to outcomes, or equivalently the latent player styles exhibited by players. Our approach is based on a Bayesian semi-parametric clustering method which has several advantages: the number of clusters do not need to be specified a priori; the technique can work with a very compact representation of each match (e.g. consisting primarily of indicator variables for player choices); a player can belong to multiple clusters and hence can have a hybrid style; and the resulting clusterings often have a straight-forward interpretation. To demonstrate the approach, we apply our method to multiplayer match logs from Battlefield 3 consisting of over 1200 players and 500,000 matches.

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Normoyle, A., & Jensen, S. T. (2015). Bayesian clustering of player styles for multiplayer games. In Proceedings of the 11th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2015 (Vol. 2015-November, pp. 163–169). The AAAI Press. https://doi.org/10.1609/aiide.v11i1.12805

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