Solving Dynamic Multi-Criteria Resource-Target Allocation Problem Under Uncertainty: A Comparison of Decomposition and Myopic Approaches
Why this work is in the frame
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Bibliographic record
Abstract
This paper is concerned with multi-criteria and dynamic resource allocation problem in a naval engagement context. The scenario under investigation considers air threats directed towards a ship that has to plan its engagement by efficiently allocating the available weapons against the threats to maximize its survivability. This dynamic and multi-criteria decision-making problem is modeled using a multi-criteria decision tree and solved with two approaches: the multi-criteria decomposition approach and the multi-criteria myopic approach. We propose a novel metric for comparing two strategies within a multi-criteria decision tree and have developed a testbed in order to simulate the engagements. The results show that, when sufficient decomposition conditions are verified, the decomposition approach produces superior decision-making strategies compared to the myopic approach. Conversely, when the multi-criteria decision aid (MCDA) method does not satisfy the decomposition conditions (e.g., TOPSIS), there is no guarantee that decomposition will provide the best compromise strategies. From a military perspective, this work will help develop tactics, procedures and training packages for such a highly complex and dynamic decision-making problem. The plans generated by the approach presented here can also serve as a reference for assessment of the quality of the engagement plans yielded by real-time planning algorithms.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it