Air traffic control trainer software development: multi-agent architecture and Java prototype
Bibliographic record
Abstract
This paper reports work on the development of a feedback system model for prototyping an air traffic control trainer (tATC). The feedback system itself represents a cleanroom engineering process designed as a closed loop containing decider, effector, and measure processes with reference values taken from a cleanroom plan and software engineering guide. The project-specific and atomic (task specific) models of a feedback system used to develop a tATC are described. Five concurrent processes (weather, airspace, aircraft, airport, and score) drive the scan subsystem of the tATC. Each of these process has an agent architecture. Each agent functions as an independent entity capable of interacting with other agents and the environment. A statechart describing the entire process structure of the tATC is given. This statechart is decomposed to reveal both the structure of subprocesses, the relationships between subprocesses of the tATC, and to provide a blueprint for tATC prototypes. The aircraft display process is described. The detailed design of the tATC is carried out with Java. The contribution of this paper is the presentation of a specific feedback system model useful in developing prototypes for a tATC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".