Intelligent Adaptive Interfaces for the Control of Multiple UAVs
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.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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 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".