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Cited by 34Artificial Intelligence in GamesDigital Games and MediaCustomer churn and segmentation

Predicting Game Difficulty and Churn Without Players

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Roohi, Shaghayegh, Relas, Asko, Takatalo, Jari, Heiskanen, Henri, Hämäläinen, Perttu·2020·Proceedings of the Annual Symposium on Computer-Human Interaction in Play
DOI 10.1145/3410404.3414235
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Abstract

We propose a novel simulation model that is able to predict the per-level churn and pass rates of Angry Birds Dream Blast, a popular mobile free-to-play game. Our primary contribution is to combine AI gameplay using Deep Reinforcement Learning (DRL) with a simulation of how the player population evolves over the levels. The AI players predict level difficulty, which is used to drive a player population model with simulated skill, persistence, and boredom. This allows us to model, e.g., how less persistent and skilled players are more sensitive to high difficulty, and how such players churn early, which makes the player population and the relation between difficulty and churn evolve level by level. Our work demonstrates that player behavior predictions produced by DRL gameplay can be significantly improved by even a very simple population-level simulation of individual player differences, without requiring costly retraining of agents or collecting new DRL gameplay data for each simulated player.

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  • Researchers proposed methods to automate the playtesting process.

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    From Playtesting: What is Beyond Personas (2023) · cites this paper as [3]

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Cited by

Sorted by citations

Authors
Roohi, S., Relas, A., Takatalo, J., Heiskanen, H., & Hämäläinen, P.
Affiliations
  • Aalto University (FI)
  • Rovio Entertainment, Espoo, Finland
ORCID
Hämäläinen, Perttu
Venue
Proceedings of the Annual Symposium on Computer-Human Interaction in Play
Publisher
ACM
Published
2020-11-02
Pages
585-593
DOI
10.1145/3410404.3414235
OpenAlex
W3081860130
Cited by
34
References
4
Citations by year
2026: 2 · 2025: 6 · 2024: 5 · 2023: 7 · 2022: 7 · 2021: 7
FWCI
1.97
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • Annual Symposium on Computer-Human Interaction in Play (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Aaltodoc (Aalto University) (submittedVersion, other-oa)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
  • Social Sciences › Sociology and Political Science › Digital Games and Media
  • Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
Keywords
free-to-play games, deep reinforcement learning, player skill, pass rates, mobile games

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