The AGILE Design of Reality Game AI
Bibliographic record
Abstract
This chapter investigates the use of agile program design techniques within an online game development laboratory setting. The proposed game concerns the prediction of early Paleo-Indian hunting sites in ancient North America along a now submerged land bridge that extended between Canada and the United States across what is now Lake Huron. While the survey of the submerged land bridge was being conducted, the online class was developing a computer game that would allow scientists to predict where sites might be located on the landscape. Crucial to this was the ability to add in gradually different levels of cognitive and decision-making capabilities for the agents. We argue that the online component of the courses was critical to supporting an agile approach here. The results of the study indeed provided a fusion of both survey and strategic information that suggest that movement of caribou was asymmetric over the landscape. Therefore, the actual positioning of human artifacts such as hunting blinds was designed to exploit caribou migration in the fall, as is observed today.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".