Bibliographic record
Abstract
Analysis and design of information systems (IS) is the process of eliciting the system’s requirements and transforming them into a model that could be used to develop IS. Analysis and design of agent-oriented information systems (AOIS) relates to the very same process using the multi-agent paradigm. A comprehensive and rigorous methodology for developing multi-agent systems is lacking [Elammari and 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]. In this paper, we describe different methodologies that are suitable for analyzing and designing AOIS. We view the analysis and design process as a modelling problem, and look at the representation models when studying the frameworks. In addition to agent-oriented Analysis and Design (A&D) methodologies, we also review the representational component of agent-oriented architectures, languages, and development frameworks. These neighboring fields provide valuable insight of what should be captured by models of an agent-oriented A&D methodology.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".