Multi-Group Role Assignment with Constraints in Adaptive Collaboration
Bibliographic record
Abstract
In many practical cases, the original task is divided into smaller, easier-to-complete tasks and assigned to different groups. Group role assignment (GRA) is dedicated to optimizing the performance of a group, which is not applicable to the multi-group role assignment (MGRA). Moreover, in dynamic scenes, the agents’ capabilities change over time, further complicating the problem. Based on the emerging and promising role-based collaboration (RBC) theory and its E-CARGO (Environments - Classes, Agents, Roles, Groups, and Objects) model, we formulate the adaptive MGRA problem, and propose a novel current state-based MGRA (CSB-MGRA) algorithm to keep the entire team productive. The constraints of the tasks are not the same due to their diverse characteristics and needs. Moreover, team members do not necessarily remain the same in the whole process, and staff transfers may occur between groups. A constant assignment scheme is not guaranteed to maximize team performance. Therefore, the constraints of different groups are set to be different, and re-assignments of the whole team are considered in the construction of CSB_MGRA. The experimental results prove the practicality of the solution proposed in this paper.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".