A Hybrid Cooperative Behavior Learning Method for a Rule-Based Shout-Ahead Architecture
Why this work is in the frame
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Bibliographic record
Abstract
We present an agent architecture and a hybrid behavior learning method for it that allows the use of communicated intentions of other agents to create agents that are able to cooperate with various configurations of other agents in fulfilling a task. Our shout-ahead architecture is based on two rule sets, one making decisions without communicated intentions and one with these intentions. Reinforcement learning is used to determine in a particular situation which set is responsible for the final decision. Evolutionary learning is used to learn these rules. Our application of this approach to learning behaviors for units in a computer game shows that the use of shout-ahead substantially improves the quality of the learned behavior compared to agents not using shout-ahead.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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 it