An efficient algorithm for task allocation with multi-agent collaboration constraints
Bibliographic record
Abstract
In this paper, we study a heterogeneous task assignment problem with a constraint on the number of collaborators. Existing work on task allocation pays little attention to the task’s requirement on the number of collaborators, so most algorithms may not work at all when this constraint is taken into account. First, this paper proposes a new task utility function that makes the traditional task allocation algorithm work properly. Then, this task allocation problem is modeled based on a game and an algorithm named IGreedyNE is proposed to solve this problem. IGreedyNE is a greedy strategy-based algorithm that allows multiple agents to change their game strategy simultaneously in each iteration, so it takes fewer iterations and less time to solve. Finally, we also show that the IGreedyNE algorithm converges in a finite number of iterations and returns a Nash equilibrium solution. We have performed numerous simulations, and the statistical results show that our proposed utility function can effectively handle the constraint on the number of cooperators, and our proposed IGreedyNE algorithm has a significant advantage in the speed of solving.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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".