Abstract
We introduce a method for automatically rating the difficulty of puzzle game levels. Our method takes multiple aspects of the levels of these games, such as level size, and combines these into a difficulty function. It can simply be adapted to most puzzle games, and we test it on three different ones: Flow, Lazors and Move. We conducted a user study to discover how difficult players find the levels of a set and use this data to train the difficulty function to match the user-provided ratings. Our experiments show that the difficulty function is capable of rating levels with an average error of approximately one point in Lazors and Move, and less than half a point in Flow, on a difficulty scale of 1-10.
Original language | English |
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Title of host publication | 2015 IEEE Conference on Computational Intelligence and Games (CIG) |
Publisher | IEEE |
Pages | 415-422 |
Number of pages | 8 |
ISBN (Print) | 978-1-4799-8621-7 |
DOIs | |
Publication status | Published - 2015 |
Keywords
- Games
- Color
- Estimation
- Rocks
- Correlation
- Time measurement
- Linear programming
- PSY
- PUZ
- AI