Rescalable, Replayable Maps Generated with Evolved Cellular Automata
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.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".