Summary of Difficulty Modelling in Mobile Puzzle Games: An Empirical Study on Different Methods to Combine Player Analytics and Simulated Data, by Jeppe Theiss Kristensen et al.
Difficulty Modelling in Mobile Puzzle Games: An Empirical Study on Different Methods to Combine Player Analytics and Simulated Data
by Jeppe Theiss Kristensen, Paolo Burelli
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a novel approach to measuring difficulty in games, which is critical for optimizing player engagement. They argue that current methods rely on post-release data analysis, providing limited insights into how future players will experience the game. The authors introduce a new metric that accounts for the characteristics of potential future players, enabling developers to fine-tune their games more effectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Gaming studios want to make sure players enjoy playing their games. One way they do this is by making it challenging but not too hard. To figure out how hard their game should be, designers often look at what happens when people play the game. But this only helps them after the game is already out. This paper shows a new way to measure how hard a game is that takes into account who might play it in the future. This will help developers make better games and keep players happy. |
