Decentralised Multi-agent Search, Track and Defence Coordination using a PMBM filter and Data-driven Robust Optimisation
Bibliographic record
Abstract
In an air defence scenario decisions need to be taken with extreme precision and under high pressure. These decisions becomes even more challenging when the aircraft in question need to function as a team and coordinate their effort. Because of the difficulty of the task, and the amount of information that needs to be rapidly processed, fighter pilots can benefit greatly from computer-assisted decision making. In this thesis this kind of decentralised multi-agent coordination problem is studied and mission assignment models, based on robust and stochastic optimisation, are evaluated. Since the information obtained by aircraft sensors often suffer from a notable amount of noise and the scenario state therefore is uncertain, a Poisson multi-Bernoulli mixture filter is implemented in order to model these noisy measurements and keep track of potential adversaries. The study finds that the filter used was more than capable of handling the scenario uncertainties and provided valuable task information to the mission assignment models. However, the preliminary robust optimisation models based entirely on the positional uncertainty of the adversaries were not sophisticated enough for such a complex coordination problem, indicating that further research is needed in this area.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".