Towards Multiple-Layer Self-Adaptations of Multi-Agent Organizations Using Reinforcement Learning
Why this work is in the frame
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Bibliographic record
Abstract
This chapter proposes a multi-agent organization model for self-adaptive software to examine the autonomous components and their self-adaptation that can be occurred at either the fine-grain behavior layer of a software agent or the coarse-grain organization layer of the roles that the agent plays. The authors design two-layer self-adaptation mechanisms and combine them with reinforcement learning together to tackle the uncertainty issues of self-adaptation, which enables software agents to make decisions on self-adaptation by learning at run-time to deal with various unanticipated changes. The reinforcement learning algorithms supporting fine-grain and coarse-grain adaptation mechanisms are designed. In order to support the development of self-adaptive software, the software architecture for individual agents, the development process and the software framework are proposed. A sample is developed in detail to illustrate our method and experiments are conducted to evaluate the effectiveness and efficiency of the proposed approach.
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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 | 0.001 |
| 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