Optimal Control and Game Theoretic Approaches to Cooperative Control of a Team of Multi-Vehicle Unmanned Systems
Why this work is in the frame
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Bibliographic record
Abstract
The main goal of this work is to design a team of multi-agent unmanned vehicles that can accomplish consensus over a common value for vehicles' output in a leaderless topology. The interaction between agents due to information exchange is modelled in the characterization of the dynamical model of each agent. The main approach to the problem is based on cooperative game theory, however in order to clarify the cooperative nature of the method, two approaches to this problem are considered: First a semi-decentralized optimal control strategy is designed based on minimization of individual costs using local information. Next, cooperative game theory is used to ensure the team cooperation by combining individual cost into a team cost function. This cooperative solution needs the full information of the team but results in lower cost for each agent. The choice of Nash-bargaining solution among a set of Pareto-efflcient solutions for this cooperative game guarantees minimum individual cost by maximizing the difference between the cooperative (centralized) and non-cooperative (semi-decentralized) individual cost.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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