Data-Driven Procedural Generation is a procedural generation paradigm that systematically integrates empirical data—such as observed patterns, statistical distributions, or learned representations from real-world datasets—into the synthesis process to constrain, guide, or parameterize the procedural rules, thereby differentiating it from purely algorithmic or hand-crafted procedural methods by grounding generated outputs in measurable data characteristics.
22
Publications
695
Citations
39
Authors
24
Institutions
Publications per year
2017–2026
8
Parents
Publications
22
1–5 of 22
Authors
39
- AS
6
h-index
- SS
5
h-index
- MM
4
h-index
- SO
3
h-index
- KC
2
h-index
1–5 of 39
Institutions
24
University of California, Santa Cruz
Santa Cruz, United States
4 h-index
6 publications
308 citations
Drexel University
Philadelphia, United States
3 h-index
4 publications
76 citations
IT University of Copenhagen
Copenhagen, Denmark
2 h-index
2 publications
36 citations
University of Manchester
Manchester, United Kingdom
2 h-index
4 publications
92 citations
Universidad del Noreste
Tampico, Mexico
2 h-index
2 publications
36 citations
Xidian University
Xi'an, China
1 h-index
1 publications
25 citations
Brazilian Society of Computational and Applied Mathematics
São Carlos, Brazil
1 h-index
4 publications
84 citations
University of Hong Kong
Pok Fu Lam, Hong Kong
1 h-index
1 publications
54 citations
1–8 of 24
Venues
- Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
6 publications · 114 citations
7
h-index
- IEEE Transactions on Computational Intelligence and AI in Games
2 publications · 58 citations
2
h-index
- arXiv (Cornell University)
Repository · 2 publications · 185 citations
2
h-index
- The Quarterly Journal of Experimental Psychology Section A
1 publications · 12 citations
1
h-index
- Expert Systems with Applications
1 publications · 21 citations
1
h-index
1–5