A Multi-Label A* Algorithm for Multi-Agent Pathfinding
Bibliographic record
Abstract
Given a set of agents, the multi-agent pathfinding problem consists in determining, for each agent, a path from its start location to its assigned goal while avoiding collisions with other agents. Recent work has studied variants of the problem in which agents are assigned a sequence of goals (tasks) that become available over time, such as the online multi-agent pickup and delivery (MAPD) problem. In this paper, we propose a multi-label A* algorithm (MLA*) for this problem. It extends the classic A* algorithm by allowing the computation of paths with multiple ordered goals (such as a pickup and delivery). Moreover, we develop a new h-value-based centralized heuristic for the MAPD. Computational experiments show that our proposed MLA* obtains substantial improvements in terms of makespan and service time as compared to existing methods, while being more computationally efficient. On instances with a thousand tasks and hundreds of agents, our method reduces the average service time by 43% compared to the state of the art, with considerably less computational effort.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".