Evaluating Player Experience in Stealth Games: Dynamic Guard Patrol Behavior Study
Bibliographic record
Abstract
In stealth games, guard patrol behavior constitutes one of the primary challenges players encounter. While most stealth games employ hard-coded guard behaviors, the same approach is not feasible for procedurally generated environments. Previous research has introduced various dynamic guard patrol behaviors; however, there needs to be more play-testing to quantitatively measure their impact on players. This research paper presents a user study to evaluate players' experiences in terms of enjoyment and difficulty when playing against several dynamic patrol behaviors in a stealth game prototype. The study aimed to determine whether players could differentiate between different guard behaviors and assess their impact on player experience. We found that players were generally capable of distinguishing between the various dynamic guard patrol behaviors in terms of difficulty and enjoyment when competing against them. The study sheds light on the nuances of player perception and experience with different guard behaviors, providing valuable insights for game developers seeking to create engaging and challenging stealth gameplay.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".