Predicting Game Difficulty and Churn Without Players

• URL: http://arxiv.org/abs/2008.12937v1
• Date: Sat, 29 Aug 2020 08:37:47 GMT
• Title: Predicting Game Difficulty and Churn Without Players
• Authors: Shaghayegh Roohi (1), Asko Relas (2), Jari Takatalo (2), Henri Heiskanen (2), Perttu H\"am\"al\"ainen (1) ((1) Aalto University, Espoo, Finland, (2) Rovio Entertainment, Espoo, Finland)
• Abstract summary: We propose a novel simulation model that is able to predict the per-level churn and pass rates of Angry Birds Dream Blast. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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