Simultaneous Dual Level Creation for Games
Bibliographic record
Abstract
Recent research has shown that it is possible to design fitness functions, based on dynamic programming, that allow evolutionary computation to automatically generate level maps for games. In this study levels with multiple types of barriers are automatically designed. The levels are designed under the assumption that there are two agent types and that at least one agent type may ignore one type of barrier. A specification of multiple types of barriers thus creates two mazes, one for each agent type, that coexist in the same space. The design of these dual mazes is accomplished using different fitness functions for two mazes simultaneously. For example, this permits a level with a single long winding path for an agent that cannot walk through one type of barrier coexisting with a low-diameter maze with more complex connectivity for an agent that cannot cross another type of barrier. This study explores two representations for game levels with multiple barrier types using four different pairs of fitness functions. The system is shown to be able to design dual mazes whose properties depend substantially on both the choice of fitness function and representation used.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".