Résolution du problème de la patrouille multi-agent en utilisant des colonies compétitives de fourmis
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
RESUME. Patrouiller dans un environnement implique une equipe d’agents dont le but consiste a visiter continuellement et aussi frequemment que possible les lieux les plus pertinents. Afin d’obtenir des performances optimales, il est alors primordial que les agents coordonnent leurs actions. De nombreux domaines peuvent etre concernes par ce probleme, comme la robotique, la simulation ou les jeux video. Nous adoptons dans cet article une approche d’optimisation basee sur les colonies de fourmis pour traiter ce probleme. Deux algorithmes sont proposes, dans lesquels des colonies de fourmis sont engagees dans une competition pour decouvrir la meilleure strategie de patrouille multi-agent. Les resultats experimentaux montrent que, sur quatre des six graphes etudies, l’une de nos techniques est significativement meilleure que la technique d’apprentissage par renforcement proposee par Santana.
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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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