Evaluating Different Genetic Operators in the Testing for Unwanted Emergent Behavior Using Evolutionary Learning of Behavior
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Bibliographic record
Abstract
We present an experimental comparison of different genetic operators regarding their use in an evolutionary learning method that searches for unwanted emergent behavior in a multi-agent system. The idea of the learning method is to evolve cooperative behavior of a group of so-called attack agents that act in the same environment as the tested agents. The attack agents use action sequences as agent architecture and the quality of a group of such agents is measured by how near their behavior brings the tested agents to show the unwanted behavior. Our experiments within the ARES II rescue simulator with an agent team written by students show that this method is able to find unwanted emergent behavior of the agents. They also show that rather standard genetic operators (on the team level and the agent level) are already sufficient to find this unwanted behavior.
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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 | 0.001 |
| 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