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Record W2396141326 · doi:10.3166/ria.30.11-33

Gestion des réseaux temporels simples multi-agents dynamiques

2016· article· fr· W2396141326 on OpenAlexvenueno aff
Guillaume Casanova, Charles Lesire, Cédric Pralet

Bibliographic record

VenueRevue d intelligence artificielle · 2016
Typearticle
Languagefr
FieldComputer Science
TopicConstraint Satisfaction and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

La realisation de plans d’activites par plusieurs agents est generalement soumise a un ensemble de contraintes temporelles, impliquant notamment des contraintes de synchronisation entre agents. L’ensemble des contraintes temporelles d’un plan distribue peut etre represente en utilisant une structure Multi-agent Simple Temporal Network (MaSTN). Dans ce papier, nous considerons le probleme du maintien de la coherence temporelle des plans distribues durant l’execution, ou les contraintes temporelles peuvent etre modifiees. Pour cela, nous proposons de nouveaux algorithmes incrementaux pour gerer les MaSTN dynamiques. Nous analysons les performances de ces algorithmes lorsque les communications sont intermittentes.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.922
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.109
GPT teacher head0.312
Teacher spread0.203 · 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; both teacher heads agree on what is shown here.

Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations0
Published2016
Admission routes1
Has abstractyes

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