Bibliographic record
Abstract
Beginning with artificial ants and including such tasks as Tartarus, software agents that are situated on a grid have been a staple of evolutionary computation. This manuscript introduces a grid-robot problem in which the agents simulate single or multiple drivers on a two-lane interstate freeway that may have obstructions. The drivers are represented as if-skip-action lists, a linear genetic programming structure. With one driver present, the problem is similar to an artificial ant task, requiring only that the grid robot learn where fixed obstacles are placed. When multiple drivers are present, the process of driving can be cast as a game similar to the prisoner's dilemma. The relative advantage to be gained from inducing another vehicle to crash is analogous to defection in the prisoner's dilemma. The game differs from prisoner's dilemma in that defecting is a complex learned behavior, not simply a move the grid robot may choose. A skilled opponent may dodge an attempt at "defection". Six sets of experiments with up to five drivers and two fixed obstacles are performed in this study. In multi-driver simulations evolution locates a diversity of behaviors within the context of the driver task
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".