@inproceedings{f6e677ebdae046509adf0459342587eb,
title = "Predicting Personas Using Mechanic Frequencies and Game State Traces",
abstract = "We investigate how to efficiently predict play personas based on playtraces. Play personas can be computed by calculating the action agreement ratio between a player and a generative model of playing behavior, a so-called procedural persona. But this is computationally expensive and assumes that appropriate procedural personas are readily available. We present two methods for estimating play personas, one using regular supervised learning and aggregate measures of game mechanics initiated, and another based on sequence learning on a trace of closely cropped gameplay observations. While both of these methods achieve high accuracy when predicting play personas defined by agreement with procedural personas, they utterly fail to predict play style as defined by the players themselves using a questionnaire. This interesting result highlights the value of using computational methods in defining play personas.",
keywords = "game mechanics, machine learning, play persona, player modeling, videogames",
author = "Green, {Michael Cerny} and Ahmed Khalifa and M. Charity and Debosmita Bhaumik and Julian Togelius",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Congress on Evolutionary Computation, CEC 2022 ; Conference date: 18-07-2022 Through 23-07-2022",
year = "2022",
doi = "10.1109/CEC55065.2022.9870406",
language = "English (US)",
series = "2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings",
}