Adaptive Action Selection in Autonomic Software Using Reinforcement Learning
Why this work is in the frame
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Bibliographic record
Abstract
The planning process in autonomic software aims at selecting an action from a finite set of alternatives for adaptation. This is an abstruse problem due to the fact that software behaviour is usually very complex with numerous number of control variables. This research work focuses on proposing a planning process and specifically an action selection technique based on "Reinforcement Learning" (RL). We argue why, how, and when RL can be beneficial for an autonomic software system. The proposed approach is applied to a simulated model of a news web application. Evaluation results show that this approach can learn to select appropriate actions in a highly dynamic environment. Furthermore, we compare this approach with another technique from the literature, and the results suggest that it can achieve similar performance in spite of no expert involvement.
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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