Intelligent Adaptive Interfaces for the Control of Multiple UAVs
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
A lack of guidance for designing complex, dynamic networked systems presents challenges to the development of such systems to maximize overall human-machine system performance. An intelligent adaptive interface (IAI) concept and associated technologies have been developed to address this problem. In order to support effective decision making, a typical IAI is driven by software agents that can change the display and/or control characteristics to react to the changes of mission and operator states in real time. This work investigated the efficacy of IAIs in a multi-uninhabited aerial vehicle (UAV) scenario. The IAI was modeled as part of the UAV tactical workstations found in a maritime patrol aircraft. A performance model was developed to compare the difference in mission activities with and without IAI agents. A prototype IAI experimental environment was implemented for a human-in-the-loop empirical investigation. Both simulation and experiment results revealed that the control of multiple UAVs is a cognitively complex task with high workload. IAIs facilitated a significant reduction in workload and an improvement in situation awareness, thus allowing operators to continue working under high time pressure. This research revealed IAI triggering conditions under different cognitive workload situations.
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 | 0.001 | 0.001 |
| 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 | 0.000 | 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 it