Artificial Intelligence in Gaming: A Bibliometric Analysis of Research Outputs and Trends
Bibliographic record
Abstract
This report provides a thorough bibliometric analysis of academic research on artificial intelligence (AI) in gaming, utilizing data obtained from the Scopus database. The analysis reveals noteworthy trends and offers unique insights into the area by analyzing publication numbers, citation impacts, authorship patterns, and geographical distribution. The most notable publication sources are highlighted, with the IEEE Conference on Computational Intelligence and Games (CIG) being the primary venue, followed by CEUR Workshop Proceedings and AAAI Workshop - Technical Report. Documents that have received a significant number of citations, such as those authored by Mnih V (2015) and Vinyals O (2019), are recognized for their pioneering contributions. Renowned writers such as Bulitko V, Togelius J, and Lucas SM are recognized for their significant contributions to the scientific community. The report also shows a significant rise in academic production after 2013, suggesting an increasing fascination and swift progress in the utilization of AI in gaming. The United States has the top position in terms of research contributions, with the United Kingdom and Canada following closely behind, demonstrating a significant global presence. The frequent utilization of terminology such as "artificial intelligence," "human-computer interaction," and "video game" highlights the research's emphasis on technology and user-centeredness. The research also recognizes constraints arising from its sole dependence on the Scopus database, which may result in the exclusion of pertinent papers that are indexed in other prominent databases such as Web of Science and Google Scholar. However, the report provides a comprehensive summary of the present condition and future prospects of AI research in gaming, serving as a basis for further investigation and advancement in this rapidly evolving sector.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.198 | 0.237 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".