A Comprehensive Model of Automated Evaluation of Difficulty in Platformer Games
Bibliographic record
Abstract
Difficulty constitutes a key component of games. It both serves as motivation to play the game and as a way to control progression. Usually, a video game offers a gradual progression in gameplay difficulty, starting with easy levels that allow players to grasp the basic mechanics and incrementally introducing more challenging obstacles as they progress. Needless to say, the success of a game heavily relies on its ability to provide a well-balanced difficulty curve and a satisfying progression. Therefore, designing a game entails a complex and time-consuming process that involves extensive playtesting. One potential approach to address this challenge is the utilization of software tools capable of automatically evaluating the difficulty of game levels. In this article, we present a comprehensive model for automatically evaluating the difficulty levels of platformer games. Our model is based on the formal calculation of static danger zones within levels and the analysis of enemy movement patterns using simulated pheromones. To validate our model, we implemented it and conducted tests using the complete set of levels from the game Super Mario Bros. Futhermore, the article includes a detailed presentation of the model, the tools developed, a comparative analysis showing the computed results, and a discussion on the limitations and advantages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".