Goal-Driven Trusted Collaborator Selection and Task Offloading in Dynamic Collaborative Systems
Bibliographic record
Abstract
Given the limited onboard resources and operational time constraints, dynamic collaboration among moving intelligent machines, such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) through task offloading has become essential for effective task completion. However, the growing offloading complexity and mismatch between task specifics and distributed resources inevitably lead to resource wastage and potential task failures. Furthermore, malicious collaborators may sneak into offloading processes, which undermines collaborative system reliability. To tackle these challenges collectively, a goal-driven trusted task offloading strategy is proposed, which efficiently matches diverse tasks to optimal distributed resources. Specifically, multidimensional goals of complex tasks are modeled as distinct task completion metrics, jointly termed Value of Service (VoS). Moreover, we define task-specific trust as a goal-achieving mechanism that enables the construction of a reliable collaborator group for a given task with diverse VoS. Based on the task-specific trust evaluation of all potential collaborators, the task offloading process is transformed into a trust-guided bipartite graph matching problem. To mitigate the matching complexity in large-scale collaborative systems, decomposed subtasks with similar goals are initially clustered into limited categories and subsequently arranged by priorities. Simulation results show the proposed strategy efficiently selects capable and reliable collaborators who complete tasks as expected in unreliable dynamic environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".