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Record W4303044269 · doi:10.1002/rnc.6381

Leaderless output sign consensus of heterogeneous multi‐agent systems over signed graphs

2022· article· en· W4303044269 on OpenAlexaff
Yihan Meng, Shimin Wang, Hongwei Zhang

Bibliographic record

VenueInternational Journal of Robust and Nonlinear Control · 2022
Typearticle
Languageen
FieldComputer Science
TopicDistributed Control Multi-Agent Systems
Canadian institutionsQueen's University
FundersNational Natural Science Foundation of China
KeywordsAdjacency matrixSign (mathematics)Multi-agent systemControl theory (sociology)Observer (physics)Computer scienceConsensusGraphController (irrigation)Directed graphSigned graphTopology (electrical circuits)State (computer science)Network topologyGraph theoryMathematicsControl (management)Theoretical computer scienceAlgorithmArtificial intelligenceCombinatorics

Abstract

fetched live from OpenAlex

Abstract This article studies leaderless output sign consensus of heterogeneous multi‐agent systems over directed signed graphs. The multi‐agent system can synchronize in the same sign concurrently with time‐varying values without an explicit leadership. Established on the assumption that the adjacency matrix of the signed graph is eventually positive, an autonomous system is constructed by utilizing a distributed sign observer. This autonomous system is not pre‐specified but arises from the graph topology and the initial states of the agents. In light of this distributed sign observer, we propose a state feedback controller and an output feedback controller that drive the outputs of such multi‐agent systems to reach sign consensus.

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)0.003
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
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.254
Teacher spread0.226 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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