Rescalable, Replayable Maps Generated with Evolved Cellular Automata
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A fashion-based cellular automata is one whose updating rule follows the form of an ecological competition model. The rule for the automata is specified by a square matrix with entries quantifying the influence each state has on each other when they both occur within a neighborhood. Because they preserve areas containing a single cell state, these rules are well able to specify automata that rapidly transform a random initial condition into a map appearing as a collection of caverns. Because the automata acts in a purely local fashion, it is valuable for generating collections of maps with similar look-and-feel, but different details, enabling automatic content generation and replayability in video games. This study extends an earlier study, examining new fitness functions and studying reusability, scalability, and the impact of parameter tuning for this type of cellular automata for automatically designing level maps. A representation for evolutionary computation is morphable if convex combinations of instances of the representation are instances of the representation. The fashion-based rules, being specified by real values matrices, are morphable. The ability to produce new, more complex maps by exploiting morphability is also explored.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it