On the Complexity of the Multi-Robot, Multi-Depot Map Visitation Problem
Why this work is in the frame
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Bibliographic record
Abstract
This paper discusses the multi-robot, multi-depot Map Visitation Problem, a multi-robot inspection problem in which a team of robots originating from multiple home base depots must visit a collection of previously identified critical locations in a two-dimensional navigation environment. In its precise focus on location inspection, it is related yet complementary to other inspection or surveillance problems such as boundary coverage or patrol. In the paper, we analyze graph representations and an agent model appropriate for the Map Visitation Problem, and we present complexity results for a variety of categories of map structures, including lines, rings, trees, and general graphs. In addition to complexity results, we present an algorithm for the Map Visitation Problem on trees that is optimal for single-robot problems and a second algorithm that is provably within a factor of two of optimal for two robots inspecting arbitrary graphs.
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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 | 0.001 | 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