Predicting Game Difficulty and Churn Without Players
This addresses game design optimization for developers by providing a low-cost method to model player behavior, though it is incremental as it builds on existing DRL and simulation techniques.
The paper tackled predicting game difficulty and churn in Angry Birds Dream Blast by combining AI gameplay with a player population simulation, showing that this approach significantly improves predictions without needing costly retraining or new data.
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.