Target Threat Assessment based on Ensembles of Multi-Criteria Decision Making Methods (Poster)
Bibliographic record
Abstract
Aerial target threat assessment is an important but stressful task carried out by operators. The work involves determining the threat of aerial targets based on various types of information about the target, such as kinematics, specifications, intelligence reports, and others. The success of threat assessment lies in the reliable fusion of this information. The recent progress in technology allows for the development of more complex decision making methodologies to take into account all types of information, latency, environments, changes in priority, and inconsistencies in the quality and availability of data sources. It also allows for the integration between different decision making methodologies in order to take advantage of the strengths of each independent methodology for different applications. Multi-criteria decision making (MCDM) methods provide a structured and intuitive framework for incorporating all available target information to evaluate the target threat. However, threat assessment is a complex process where there is not one MCDM method that works in all situations. The objective of this work is to develop a robust system that uses various MCDM methods as an ensemble (i.e. an EMCDM technique) and machine learning techniques to assess the threat of aerial targets. Experiments involving threat assessment on simulated targets indicate the benefit of using EMCDM techniques instead of using individual MCDM methods for performing high-level information fusion to assign threat values.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".