Predicting Game Difficulty and Churn Without Players

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.

Keywords

player modeling, churn prediction, game AI

DOI

10.1145/3410404.3414235

Citation

Roohi, S, Relas, A, Takatalo, J, Heiskanen, H & Hämäläinen, P 2020, Predicting Game Difficulty and Churn Without Players . in CHI PLAY 2020 - Proceedings of the Annual Symposium on Computer-Human Interaction in Play . ACM, pp. 585-593, ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play, Virtual, Online, Canada, 01/11/2020 . https://doi.org/10.1145/3410404.3414235

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-2020123160449

Collections

[article-cris] Perustieteiden korkeakoulu / SCI

Show all metadata

Predicting Game Difficulty and Churn Without Players

Access rights

URL

Journal Title

Journal ISSN

Volume Title

Authors

Date

Department

Major/Subject

Mcode

Degree programme

Language

Pages

Series

Abstract

Description

Keywords

Other note

DOI

Citation

Permanent link to this item

Collections