Balancing multiplayer first-person shooter games using aiming assistance
Bibliographic record
Abstract
When player skill levels are different in competitive First Person Shooter (FPS) games, the weaker player can end up feeling discouraged and the stronger player may get bored with the lack of challenge - ultimately yielding a non-optimal play experience for both players. Previous work has investigated how aiming assistance can be applied in a 3D environment to assist weaker players; however, there is little information on whether aiming assistance balances gameplay in a multiplayer environment or on how aim assistance affects player experience. We carried out a study to test the effectiveness of two aim assistance techniques (Bullet Magnetism and Area Cursor) that have been shown to help aiming in a 3D FPS. Our study had novice-expert pairs play deathmatch games in a multiplayer 3D FPS with and without the assistance techniques. The study showed that although Area Cursor and Bullet Magnetism resulted in better performance of the weaker player, it did not result in closer scores, and had no effect on the players' enjoyment of the game. Our results indicate that balancing performance in 3D FPS games is more complex than simply helping weaker players' aim, and we suggest possible reasons that warrant further investigation. This study is the first realistic evaluation of balancing techniques for 3D First Person Shooters, providing empirical evidence of the difficulty of using aim assistance techniques for balancing competitive gameplay.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".