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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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 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".