Adjustable Control for Enhanced Interoperability of Human-Autonomy Teaming Within ISR Operations
Bibliographic record
Abstract
The development of autonomous vehicles such as Unmanned Aerial Systems (UAS) are becoming a major tool for air and information superiority in defence and threat anticipation. Human-Autonomy Teaming (HAT) systems are crucial for the success of team collaboration, trust and mission execution in command and control (C2) operations such as Intelligence, Surveillance and Reconnaissance (ISR). The present work focuses on understanding when and how control should be allocated to autonomous agents. Intelligent adaptive control methods between human and autonomous agents are introduced. The proposed method consists in developing a HAT system based on the co-active design framework. A prototype system was implemented and tested in a human-in-the-loop experiment involving a simulated ISR mission. Findings helped assess individual capacities and detect behavioural patterns for adaptive control. More specifically, it helped recommend the level of support and control to be allocated for each agent. Results focused on the human operator and helped gain insight about how task-allocation strategies could be implemented within a complex ISR operation by breaking it down into sub-tasks. A key outcome of this work to help augment team interoperability in C2 operations. This paper was originally presented at the NATO Science and Technology Organization Symposium (ICMCIS) organised by the Information Systems Technology (IST) Panel, IST-205-RSY - the ICMCIS, held in Koblenz, Germany, 23–24 April 2024.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".