A hierarchical decision and information system for multi-aircraft combat missions
Bibliographic record
Abstract
Designing decision, control and information systems is motivated, in part, by the need to support the deployment of multiple aircraft, such as combat vehicles, unmanned combat air vehicles, unmanned aerial vehicles, and weapons, in missions taking place in a dynamic, although uncertain, environment. Such systems aim at ensuring mission success without overloading the operating crew, the pilots, and the commanders. One of the main design challenges lies in obtaining some sort of coherent behaviour of the fleet, by means of solutions to potentially NP-hard problems, given incomplete and imperfect information, and despite limited computational and communication capabilities. In this context, this article proposes a hierarchical decision and information system aiming at providing, in real-time, coordinated aircraft path planning and deceptive engagement assignments. The blue—red engagement policy is obtained by minimizing, and balancing, the energy expenditure among the vehicles while constraining information exchanges to a minimum defined by a risk of inconsistency. The proposed system relies on dynamic programming, online heuristic techniques and stochastic, consistency-checking methods. Numerical simulations show that the proposed approach compares advantageously to a random process and to a law that seeks to minimize the cost of the confrontation at a given time regardless of past moves. However, there is a trade-off between increasing the level of deception and the level of energy consumption.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".