Semi-Decentralized Optimal Control of a Cooperative Team of Agents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The main goal of this work is to design a decentralized optimal control for a team of multi-agents that can accomplish consensus in a leaderless structure. Towards this end, a semi-decentralized optimal control strategy is designed based on minimization of individual cost functions using local information and based on solving HJB equations. The interaction between agents due to information flow is modelled in characterization of dynamical model of each agent and for this purpose the control input is divided into two parts. One part is designed based on the agent'_ own state and the second part is a function of information from neighboring agents of each agent. Effectively, the consensus algorithm is derived in a formal way that is based on conventional control methodologies. Finally, the simulation results are presented to show effectiveness of the proposed method in achieving predefined requirements.
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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 | 0.000 | 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