Generation of rule-based adaptive strategies for a collaborative virtual simulation environment
Why this work is in the frame
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Bibliographic record
Abstract
Real Time Strategy Games (RTSG) are a strong test bed for AI research, particularly on the subject of unsupervised learning. They offer a challenging, dynamic environment with complex problems that often have no perfect solutions. Learning classifier systems are rule-based machine learning techniques that rely on a genetic algorithm to discover a knowledge map used to classify an input space into a set of actions. This paper focuses on the use of accuracy-based learning classifier system (XCS) as the learning mechanism for generating adaptive strategies in a real time strategy game. The performance and adaptability of the developed strategies with the XCS is analyzed by facing these against scripted opponents on an open source game called Wargus. Results show that the XCS module is able to learn adaptive strategies effectively and achieve the objectives of each training scenario.
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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