Using Argumentative Agents to Manage Communities of Web Services
Bibliographic record
Abstract
This paper presents a framework for specifying Web services communities. A Web service is an accessible application that humans, software agents, and other applications in general can discover, compose, and invoke in order to satisfy users' needs like hotel booking. Web services providing the same functionality are gathered into one community, independently of their origins. This framework shows how software agents that are able to argue, negotiate, and reason about Web services can be used to specify these Web services and to manage their respective communities. The use of what we call argumentative agents helps Web services in being better organized within communities and in achieving the goals for which they are conceived. The community is led by a master component, which among others attracts new Web services to the community, retains existing Web services in the community, and identifies the Web services in the community that will participate in composite Web services. All these operations are managed by interacting agents through flexible conversations made up by argumentation, persuasion, and negotiation phases called dialogue games.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".