Multi-Agent Path Finding with Task Assignment and Supporting Constraints
Bibliographic record
Abstract
The Multi-Agent Path Finding with Task Assignment (MAPF-TA) problem combines task allocation and collision-free path finding for multiple agents within a graph. It can be solved by an extension of the well-known Conflict-Based Search (CBS) algorithm called CBS-TA, which has been demonstrated to be optimal in terms of the sum of costs of all agents. While coordination between agents in MAPF-TA is limited to no-collision constraints, real-world scenarios may require cooperation between agents. For instance, in an exploration mission involving a system of multiple robots, one robot might need to enter a hazardous area only if another agent is able to monitor this area from a support location. Given a hazardous location and its corresponding support location, this coordination requirement can be modeled by a support constraint. In this paper, we propose an extension of the CBS-TA algorithm to handle these support conflicts. In addition, we improve the algorithm’s performance by introducing an alternative cost matrix for task assignment, which takes into account support coordination while maintaining the optimality of the CBS-TA algorithm. We compare the proposed approach to a greedy algorithm in which the task assignment and path finding problems are decoupled, and using two different assignment matrices: the original matrix of CBS-TA and our support-aware matrix. Experiments are carried out using standard MAPF benchmark instances, showing that the proposed cost matrix improves search time and increases the number of instances solved within a given timeout.
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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 | 0.000 |
| 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) | 0.003 | 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".