Agent-Oriented Methodologies - Towards A Challenge Exemplar.
Bibliographic record
Abstract
The agent-oriented approach to software development is transitioning from the prototyping done by researchers to the development of large-scale industrial-strength applications by software professionals. For this to succeed, methodologies are needed to systematically guide and support developers through the various stages of system development. A number of agent-oriented methodologies have been proposed recently, offering a variety of conceptual frameworks, notations, techniques, and methodological steps. The diversity of approaches offers rich resources for developers to draw on, but can also be a hindrance to progress if their commonalities and divergences are not readily understood. One way to establish a common context for probing and relating various methodologies is to define and adopt a standardized example setting (or "exemplar") to focus discussion and debate. This paper proposes an exemplar from the health care domain. It is structured into a set of scenarios, supplemented by a series of questions to be posed to each methodology. We consider how an exemplar might serve the needs of the agent-oriented methodology community, and discuss the criteria for selecting an exemplar.
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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 | 0.025 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.010 |
| 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".