Les réseaux d'engagements comme méthode pour modéliser le comportement dialogique des agents.
Bibliographic record
Abstract
In this article, we present the web of commitment methodology that allows us to specify the dialogical behavior of agents from the commitments that can be contracted between them and from the links that can exist between those commitments. At first, we present the DIAGAL agent communication language which is based on social commitments and dialogue games that are defined as structures regulating the mechanism under which some commitments are discussed through the dialogue. Then, we present our social commitments model to explain how the agent who uses DIAGAL can use the dialogue games to manipulate the commitments. For that, we introduce the web of commitments concept which makes it possible to specify the causality links that exist between various commitments of a multi-agent system. Finally, we explain using an illustrative example how we could implement, through our simulator, our concepts and ideas. MOTS-CLES : langages de communication agent, protocoles d’interaction, dialogue, modelisation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".