Automatic Generation of Diverse Cavern Maps with Morphing Cellular Automata
Why this work is in the frame
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Bibliographic record
Abstract
Cellular automata can be used to rapidly generate complex images, but controlling the character of those images can be difficult. This study continues experimentation with fashion-based cellular automata that generate cavern-like level maps and provides the beginning of a mathematical theory. Fashion-based automata are defined by a competition matrix with different cell states competing to capture territory. This study co-evolves pairs of competition matrices to permit the evolution of automata rules that can be spatially morphed to provide substantially more diverse types of maps than earlier systems using fashion-based cellular automata. As in earlier studies, the cellular automata rules function in local neighborhoods, meaning that the level generation system scales smoothly to any desired level map size. This reusability also permits variation of the type of morph used: a variety of spatial morphing styles are tested with the evolved rules. The theoretical treatment includes the derivation of a normal form for the cellular automata rules that informs the design of the fitness function and has application to understanding the fitness landscape of fashion based automata.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it