Predicting Game Difficulty and Churn Without Players

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.

9 pages, 9 figures, In Proceedings of the Annual Symposium on Computer-Human Interaction in Play (CHI PLAY '20)

References in corpus (12)

• Playing Atari with Deep Reinforcement Learning
• StarCraft II: A New Challenge for Reinforcement Learning
• The CMA Evolution Strategy: A Tutorial
• Unity: A General Platform for Intelligent Agents
• A Survey of Deep Reinforcement Learning in Video Games
• Playing FPS Games with Deep Reinforcement Learning
• Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using Survival Ensembles
• Games and Big Data: A Scalable Multi-Dimensional Churn Prediction Model
• Game Data Mining Competition on Churn Prediction and Survival Analysis using Commercial Game Log Data
• Combining Sequential and Aggregated Data for Churn Prediction in Casual Freemium Games
• Winning Isn't Everything: Enhancing Game Development with Intelligent Agents
• The Physics of Fun: Quantifying Human Engagement into Playful Activities

Cited by in corpus (4)

• Self-Determination Theory and HCI Games Research: Unfulfilled Promises and Unquestioned Paradigms
• Predicting Game Engagement and Difficulty Using AI Players
• Personalized Game Difficulty Prediction Using Factorization Machines
• The Difficulty of Novelty Detection in Open-World Physical Domains: An Application to Angry Birds