Predicting Game Difficulty and Churn Without Players

Shaghayegh Roohi, Asko Relas, Jari Takatalo, Henri Heiskanen, Perttu Hämäläinen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

6 Citations (Scopus)
138 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationCHI PLAY 2020 - Proceedings of the Annual Symposium on Computer-Human Interaction in Play
Number of pages9
ISBN (Electronic)9781450380744
ISBN (Print)9781450380744
Publication statusPublished - 2 Nov 2020
MoE publication typeA4 Article in a conference publication
EventACM SIGCHI Annual Symposium on Computer-Human Interaction in Play - Virtual, Online, Canada
Duration: 1 Nov 20204 Nov 2020
Conference number: 7


ConferenceACM SIGCHI Annual Symposium on Computer-Human Interaction in Play
Abbreviated titleCHI PLAY
CityVirtual, Online


  • player modeling
  • churn prediction
  • game AI


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