Reflective Collaborative Agents for Complex Service Integration
Bibliographic record
Abstract
With the advent of more and more services available on the Web, a user can have a difficult job of assembling the various pieces of a complex task to arrive at a final solution. Not only would the user need to access each web-based resource through its individual client-side interface, but she would also need to interpret its response to her request, and to manually combine the multiple responses from the different resources to accomplish the complex task. In this paper, we discuss a multiagent, XML-based framework that supports the development of aggregate applications that rely on semantic-based reflective monitoring and collaboration among several agents to complete the user’s task. Our framework makes use of declarative models of the domain information, the task-specific information, and the semantic constraints of this information. Each agent uses these models to interact with the user, to coordinate the information exchange with the various web resources, to monitor and control the execution of the applications, and to take action when a failure is detected. When an agent detects a failure, it collaborates with other agents by distributing the tasks to those agents which are capable of completing the task, thus ensuring successful completion of the user’s request. We illustrate our approach and the architecture of the aggregate applications that it produces using a book-buying assistant as an example. 1. Motivation and
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".