MétaCan
Menu
Back to cohort
Record W2160726730 · doi:10.1109/icsmc.2007.4413765

Optimal cooperation in a modified leader-follower team of agents with partial availability of leader command

2007· article· en· W2160726730 on OpenAlexaff
Elham Semsar-Kazerooni, K. Khorasani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed Control Multi-Agent Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsRobustness (evolution)MinificationComputer scienceControl theory (sociology)Function (biology)Control (management)Mathematical optimizationArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

The objective of this work is to design a controller for a team of agents to accomplish a cohesive motion with consensus on the agreed upon output. A modified leader-follower structure for the team is considered. The desired output (command) is available to only the leader and followers are communicating with the leader and among themselves in a predefined topology. A semi-decentralized optimal control strategy is designed based on minimization of individual cost functions over a finite horizon using local information. Decentralization is achieved through incorporating interaction terms in the team members model. Minimization of the proposed cost function results in a modified consensus algorithm for a leader-follower structure when the leader command is available partially. Introduction of a corrective feedback from followers to the leader has potential advantage of improving robustness of the team to individuals faults. Finally, simulation results are presented to show effectiveness of our proposed method in achieving predefined requirements.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.266
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2007
Admission routes1
Has abstractyes

Explore more

Same topicDistributed Control Multi-Agent SystemsFrench-language works237,207