A Weighted‐Tree Similarity Algorithm for Multi‐Agent Systems in E‐Business Environments
Why this work is in the frame
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Bibliographic record
Abstract
A tree similarity algorithm for match‐making of agents in e‐Business environments is presented. Product/service descriptions of seller and buyer agents are represented as node‐labeled, arc‐labeled, arc‐weighted trees. A similarity algorithm for such trees is developed as the basis for semantic match‐making in a virtual marketplace. The trees are exchanged using an XML serialization in Object‐Oriented RuleML. Correspondingly, we use the declarative language Relfun to implement the similarity algorithm as a parameterized, recursive functional program. Three main recursive functions perform a top‐down traversal of trees and the bottom‐up computation of similarity. Results from our experiments aiming to match buyers and sellers are found to be effective and promising for e‐Business/e‐Learning environments. The algorithm can be applied in all environments where weighted trees are used.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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