Difficulty Modelling in Mobile Puzzle Games: An Empirical Study on Different Methods to Combine Player Analytics and Simulated Data
CoRR(2024)
摘要
Difficulty is one of the key drivers of player engagement and it is often one
of the aspects that designers tweak most to optimise the player experience;
operationalising it is, therefore, a crucial task for game development studios.
A common practice consists of creating metrics out of data collected by player
interactions with the content; however, this allows for estimation only after
the content is released and does not consider the characteristics of potential
future players.
In this article, we present a number of potential solutions for the
estimation of difficulty under such conditions, and we showcase the results of
a comparative study intended to understand which method and which types of data
perform better in different scenarios.
The results reveal that models trained on a combination of cohort statistics
and simulated data produce the most accurate estimations of difficulty in all
scenarios. Furthermore, among these models, artificial neural networks show the
most consistent results.
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