Approximate message passing algorithm for decentralised task assignment and scheduling
Bibliographic record
Abstract
This paper proposes a decentralised algorithm, called task assignment and scheduling via approximate message passing (TAS-AMP), to address the task assignment and scheduling (TAS) problem in multi-agent systems. Approximate message passing (AMP) is a distributed algorithm developed for vehicle routing problems and is based on belief propagation in graphical models. Leveraging the framework of AMP while addressing its convergence limitations, TAS-AMP rapidly generates near-optimal task assignments and execution schedules by iteratively exchanging local messages among agents. To improve message convergence and solution quality, TAS-AMP introduces two key mechanisms: a pruning process and a conflict resolution phase. The pruning process refines each agent's schedule from the previous iteration using updated message values. This prevents unnecessary schedule reinitialization and thereby improves message convergence and solution stability. The conflict resolution phase reduces unassigned tasks and removes redundant assignments, ensuring a conflict-free solution. An ablation study and convergence analysis were conducted on various TAS-AMP configurations to validate the effectiveness of these mechanisms. Furthermore, numerical comparisons across diverse TAS instances demonstrated that TAS-AMP achieves enhanced solution quality and computational efficiency even under high reward heterogeneity.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".