Search-and-Fetch with 2 Robots on a Disk: Wireless and Face-to-Face Communication Models
Bibliographic record
Abstract
<p> </p> <p>We initiate the study of a problem on searching and fetching, motivated by real-life surveillance and search-and-rescue operations where unmanned vehicles, e.g. drones, search for victims in areas of a disaster. In treasure-evacuation, we are interested in designing algorithms that minimize the time it takes for a treasure (a victim) to be discovered and brought (fetched) to the exit (shelter) by any of two robots (rescuers) which are performing in a distributed environment (the case of searching and fetching with 1 robot has been previously considered).The communication protocol between the robots is either wireless, where information is shared at any time, or face-to-face, where information can be shared only if the robots meet. For both models we obtain upper bounds for fetching the treasure to the exit. Our algorithms make explicit use of the distance between the treasure and the exit, which is assumed to be known in advance, showing this way how partial information of the unknown input can be beneficial. Our main technical contribution pertains to the face-to-face model. More specifically, we demonstrate how robots can exchange information without meeting, effectively achieving a highly efficient treasure-evacuation protocol which is minimally affected by the lack of distant communication. Finally, we complement our positive results above by providing a lower bound in the face-to-face model.</p>
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".