Enhancing agent sociability by extending interaction protocols using machine learning
Bibliographic record
Abstract
Interaction protocols are commonly used in agent-based systems. They ensure good coordination between agents by proposing a specific message exchange pattern. However, these interaction protocols are not perfect; they need more extensions to offer, among others, better performance and scalability, mainly when tight deadlines are involved. In this case, participants often fail to answer some requests before their deadlines due to overload, bottlenecks, slow network, or being busy or blocked. Designing agents without considering this issue may decrease their sociability, which wastes valuable chances to obtain the best goals. The proposed approach uses the participant's experience to train supervised learning models to predict if the replies will reach initiators before deadlines or not, thereby enabling a prioritization mechanism for handling interaction requests more effectively. The proposed approach has been evaluated using multiple Contract Net interaction scenarios of two case studies under the JADE platform. The promising results show a significant increase in agents’ sociability measured by a new metric that we have proposed called Sociability Degree via Interaction Protocols (SD IP ) where it was maintained even when systems scale up in term of number of agents and initiated interactions.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".