A NOVEL HYBRID NAVIGATION SCHEME FOR RECONFIGURABLE MULTI-AGENT TEAMS
Bibliographic record
Abstract
In this paper, we propose a hybrid navigation scheme for reconfigurable multi-agent teams. To accomplish a complex task such as search and rescue, agents need to frequently adjust their roles over time according to changes in the task space and in the environment. Furthermore, when exploring an unknown environment, the loss of a team member is likely and the addition of new team members to replace that loss is often necessary. Nevertheless, the loss and addition of members in the agent team should not affect the completion of the task. Our hybrid navigation scheme, consisting of a built-in reconfiguration mechanism and mode-switching navigation functions, reflects these needs by allowing an agent team to reconfigure itself to effectively complete a wide range of tasks. Our design has been implemented in C++ and has been tested by simulation in several typical tasks. We also investigate the effects of imperfect communication on the robustness of the navigation scheme.
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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 | 0.000 | 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 | 0.001 |
| 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".