Landscape automata for search based procedural content generation
Why this work is in the frame
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Bibliographic record
Abstract
This study introduces a new representation landscape automata for encoding heightmaps that may be used for terrain generation or other procedural content generation. Landscape automata are evolvable state-conditioned quadtrees with embedded decay parameters. Landscape automata are used to both match idealized landforms and to generate a heightmap with controllable connectivity for agents using the height map as terrain. Parameter studies on both mutation rate and number of states in the automata are performed. Mutation rate is found to have a modest impact on performance while the number of states used both has a large impact on fitness and a different type of impact for each of two types of fitness functions. Landscape automata are demonstrated to be well able to match idealized landforms, providing a palette of varied approximations with a variety of secondary features. They are also able to generate heightmaps that, viewed as terrain, form challenging mazes.
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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 | 0.000 | 0.000 |
| 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