Limitations and Advantages of Autonomy in Controlling Multiple Systems: an International View
Bibliographic record
Abstract
This panel has been designed to discuss future directions in unmanned system (UMS) and human-robot interaction (HRI) research. It will focus on broad issues related to controlling multiple UMSs in the military domain from the perspective of different international studies. The members of this panel have been chosen specifically for their background and insight into these areas and it is hoped that their discussion will provide both direction and guidance necessary to overcome the challenges of UMS integration into military environments. We will discuss the following future systems (a) Israeli research on the effects of UMS visualizations from multiple systems for intelligencegathering operations by dismounted soldiers (b) Canadian research conducted on synergy between intelligent agents and war fighters, (c) problems with autonomy and possible human factors solutions using common interface solutions being developed by US Navy researchers for surface ship and ground applications, (d) the use of co-operative cognitive agent technology developed by the German Bundeswhre University as a middle ground between manual and autonomous control and (e) US Army research on progressive autonomy.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".