An intelligent-agent architecture for flexible service integration on the web
Bibliographic record
Abstract
A plethora of information and services is available on the World Wide Web; the challenge has now become to enable the interoperation of these services in the context of high-quality, integrated applications, providing personalized value-added services to the end user. TaMeX is a software framework that supports the development of intelligent multiagent applications, integrating services of existing web applications. The TaMeX applications rely on a set of specifications of the domain model, the integration workflow, their semantic constraints, the end-user profiles, and the services of the existing web applications; all these models are declaratively represented in the XML-based TaMeX integration-specification language. At run-time, the TaMeX agents use these models to flexibly interact with the end users, monitor and control the execution of the underlying applications' services and coordinate the information exchange among them, and to collaborate with each other to react to failures and effectively accomplish the desired user request. In this paper, we describe the TaMeX framework and we illustrate its capabilities with an integrated book-finding application as a case study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".