Independent activity and local opportunity for dynamic robot team management in dangerous domains
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dangerous domains are a challenge for teams of heterogeneous robots, since robot losses may involve the loss of particular skills that might be rare in the domain. Previous research has resulted in a framework that allows teams to rebalance and recruit from the environment. However, there is currently no consideration of situations where agents may at times provide more useful work globally by not joining a team, or situations where it might be discovered that types of work might be associated with a given locality. My thesis extends this framework to give agents the ability to refuse to join teams and work for times on their own, by considering current satisfaction in the use of their skills, the likely rarity of their skills, and the distribution of places those skills are used in the environment. I examine this work in a simulated Urban Search and Rescue domain. My results show that in scenarios where a robot’s special skills are rare and tasks requiring those skills are only available at a few fixed locations in the environment, a robot is more useful if it suspends its team commitment to make itself available to all teams.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it