Bibliographic record
Abstract
Analysis and design of Information Systems (ISs) is the process of eliciting the system's requirements and transforming them into a model that can be used to develop ISs. Analysis and design of Agent-Oriented Information Systems (AOISs) relates to the very same process using the multi-agent paradigm. A comprehensive and rigorous methodology for developing multi-agent systems is lacking (Elammari & Lalonde, 1999; Odell et al ., 2000). Most existing multi-agent systems were developed in an ad-hoc manner, and systems developers paid little attention to requirements specification and the analysis process (Treur, 1999a). The paper has two goals: (a) to provide an overview and (b) to discuss challenges and future research of the field. To address the first goal, we review different methodologies that are suitable for analysing and designing AOIS. This is done by examining, for each methodology, its suitability in supporting the early phases of the software engineering process (specifically analysis and design) as well as its capabilities for modelling agent-oriented systems. To address the second goal, we analyse the limitations of existing approaches, identify critical issues and point to what we think are possible future directions.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".