Design Techniques for Planning Navigational Systems in 3-D Video Games
Bibliographic record
Abstract
Navigation is an essential element of many high-budget games (known as AAA titles). In such products, players are expected to walk through and interact with aesthetically rich 3-D spaces. Therefore, designers should provide meaningful information to guide the users within a challenging environment. While there has been much research on both games and 3-D environments, there is very little research investigating design techniques used to guide players through 3-D game worlds. This paper is focused on proposing a set of navigational patterns or techniques currently used in commercial 3-D action-adventure titles. These design techniques are composed of [a] 21 patterns used to aid navigation, [b] three level design choices affecting navigation, and [c] eight game mechanics related to navigation. We uncovered these design techniques through a detailed analysis of 21 3-D action-adventure games. This contribution has several important facets. First, the set of design techniques and terminology proposed here can be used as a training construct to teach 3-D game and environment design. Second, it can also be used as a toolset for designers. Third, it will provide an important start for a formal vocabulary that can be used by designers and researchers discussing navigation in 3-D games.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".