Investigating the Influence of Behaviors and Dialogs on Player Enjoyment in Stealth Games
Bibliographic record
Abstract
The player's perception of AI behavior significantly influences their overall game experience. This perception is shaped by both interactive encounters and careful observations, particularly in genres like stealth, where gameplay revolves around planning strategies based on AI enemy movement. This paper aims to derive general insights into the player experience concerning two crucial gameplay elements that impact the perception of NPC intelligence. The first element pertains to the actual behavior of opponent NPCs, while the second focuses on the dialogues employed to highlight NPC decision-making. We conducted a user study to assess whether players can discern between complex and simple NPC behavior during gameplay in a specific scenario of a top-down stealth game prototype. We introduced variations in spoken dialogs to determine their effect on player perception. In the end, our findings revealed that when simple dialogs were used, players derived greater enjoyment from a more complex AI behavior. However, using contextual dialog allowed a simple behavior to match a complex behavior in player enjoyment.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".