Economically Inspired Self-healing Model for Multi-Agent Systems
Bibliographic record
Abstract
Self-healing in fault tolerant multi-agent systems is the system ability to automatically detect, diagnose, and repair the faults. However, most of the available solutions are fragile in incomplete, uncertain, and dynamic situations. This paper proposes a novel economically inspired self-healing model for fault tolerant Multi-Agent Systems where the agents are self-interested autonomic elements collaborate to achieve fault tolerance as a given high-level objective of the system. It is an effective solution for dynamic situations with a high possibility of uncertainty. The proposed model is in fact toward responding the challenge of negotiation theory for autonomic systems introduced by IBM. In particular, it is an inspiration of general explanation of Communism, Socialism, and Capitalism. Extensive experiments illustrate the effectiveness of the proposed social approach in comparison to the cases of no help situation, using purely redundant components, and helping without using a social value.
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How this classification was reachedexpand
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 | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".