Distributed Satellite Collection Scheduling optimization using Cooperative Coevolution and Market-Based Techniques
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose and adapt decision model formulations and algorithms suitable to the distributed satellite collection tasking problem. The decentralized multi-satellite scheduling problem setting comprises multiple stakeholders having control on their own resources to be coordinated in a time-constrained uncertain environment. Aimed at maximizing global (system-wide) and local collection value objectives, satellite platform agents are assumed to have sufficient on-board processing and decision-making capability. Agent's attitude may be defined over a mixed spectrum of cooperative goal-based behaviors. Two novel collection tasking coordination approaches relying on market-based and cooperative co-evolution mechanisms are introduced. A basic description is given for both approaches while depicting how competitive, cooperative and mixed agent attitudes are handled. Computational results reporting comparative performance show the value of the proposed solutions.
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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 | 0.000 |
| 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