Bottom-Up Mechanism and Improved Contract Net Protocol for Dynamic Task Planning of Heterogeneous Earth Observation Resources
Bibliographic record
Abstract
Earth observation resources are becoming increasingly indispensable in disaster relief, damage assessment, and other related domains. Many unpredictable factors, such as changes in observation task requirements, bad weather, and resource malfunctions, may cause the scheduled observation scheme to become infeasible. In these cases, it is crucial to promptly reformulate high-quality observation schemes while exerting minimal negative effects on the previously scheduled tasks. Accordingly, in this study, a bottom-up distributed coordination framework together with an improved contract net is proposed, aiming to facilitate dynamic task replanning for heterogeneous Earth observation resources. This hierarchical framework consists of three levels: 1) neighboring resource coordination; 2) single planning center coordination; and 3) multiple planning center coordination. The observation tasks affected by unpredicted factors are managed along with a bottom-up route from resources to planning centers. This bottom-up distributed coordination framework transfers part of the computing load to various nodes of the observation systems to plan tasks more efficiently and robustly. To support the prompt replanning of multiple tasks to proper Earth observation resources in dynamic environments, we propose a multiround combinatorial allocation (MCA) method. Moreover, a new float interval-based local search algorithm is proposed to quickly obtain a promising replanning scheme. The simulation results demonstrate that the MCA method can achieve a better task completion rate for large-scale tasks with satisfactory time efficiency. In addition, this method can efficiently obtain replanning schemes based on original schemes in dynamic environments.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".