The Impact of Agent Density and Environmental Factors on Target Tracking Swarms
Bibliographic record
Abstract
When sizing a multi-robot swarm, a key quantity to be considered is the swarm’s agent density. In the field of multi-robot and multi-agent systems, it has been acknowledged that there is a minimum agent density to ensure the emergence of cooperative behaviors, implying that too few agents within a swarm would yield an ineffective system. However, too large a swarm may result in the agents interfering with each other’s actions, again resulting in subpar swarm performances. There is therefore a range of densities where swarm operations are optimal. In this study, we investigate the factors that determine this range for collective target-tracking tasks. Specifically, we show how the use of agent-based memory can reduce the density at which swarms are able to start tracking. We also show that besides strategy design, other environmental factors affect the range of densities over which swarms can operate efficaciously, such as a target’s movement policy, its velocity, and the number of targets to be tracked.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".