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Difficulty Modelling in Mobile Puzzle Games: An Empirical Study on Different Methods to Combine Player Analytics and Simulated Data
Difficulty is one of the key drivers of player engagement and it is often one of the aspects that designers tweak most to optimise the player experience; operationalising it is, therefore, a crucial task for game development studios.
Estimating player completion rate in mobile puzzle games using reinforcement learning
In this work we investigate whether it is plausible to use the performance of a reinforcement learning (RL) agent to estimate the difficulty measured as the player completion rate of different levels in the mobile puzzle game Lily's Garden. For this purpose we train an RL agent and measure the number of moves required to complete a level.
Combining Sequential and Aggregated Data for Churn Prediction in Casual Freemium Games
In freemium games, the revenue from a player comes from the in-app purchases made and the advertisement to which that player is exposed.
Personalized Game Difficulty Prediction Using Factorization Machines
The accurate and personalized estimation of task difficulty provides many opportunities for optimizing user experience.
Statistical Modelling of Level Difficulty in Puzzle Games
The model is fitted and evaluated on a dataset collected from the game Lily's Garden by Tactile Games, and the results of the evaluation show that the it is able to describe and explain difficulty in a vast majority of the levels.
Strategies for Using Proximal Policy Optimization in Mobile Puzzle Games
While traditionally a labour intensive task, the testing of game content is progressively becoming more automated.
