Utilising artificial intelligence in a 3D video game environment design and creation process
Bibliographic record
Abstract
The objective of the thesis was to demonstrate the usage of various artificial intelligence (AI) programs in a 3D video game environment design pipeline. The author created two different environment versions based on a specific concept and visual theme. The first variation did not include any help from artificial intelligence, whereas the second attempted to rely on AI as much as possible. \nAs for the processual method used in this thesis, action research was chosen to establish a realistic and a detailed view of the environment creation pipeline. With it, the problems and solutions of the process were communicated using visual examples during the research. From the suitable qualitative methods available, comparative analysis was used to provide an overview of AI’s capabilities and effects on the environment creation process versus the author’s work executed without artificial intelligence tools. \nThe study showed that incorporating artificial intelligence in the environment design workflow comes with some setbacks, concerning ethical dilemmas and potential misinformation. Regardless its negative qualities, the AI programs managed to provide useful ideas and generate usable assets for the environment. The included AI programs were also capable of understanding composition, environmental storytelling, and video game context. Therefore, the creative process of constructing an environment was enhanced by the AI and it successfully functioned in a role of an assistant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".