The integrated modeling of multi-agent systems and their environment
Bibliographic record
Abstract
Many methodologies have been developed to support the design phase of Multi-Agent Systems (MAS). Among them, are AUML [4], Gaia [6] and MAS-CommonKADS [1]. These methodologies offer a set of diagrams to help conceptualize and represent the system under development. A MAS may evolve in a dynamic environment which it must be reactive to. A design methodology should help the designer to represent this kind of information about a changing environment and its effects on the MAS, an aspect of the modeling task which is currently lacking from the mentioned methodologies. We propose to add, to MAS modeling methodologies, two new diagrams: an environment diagram and an agent diagram. The environment diagram is a state transition diagram where each state is represented as a set of critical parameters. The agent diagram shows the organization of the system according to the roles of the agents, their tasks and their relationships expressed in the form of control, collaboration and communication relations, in relation to the environment diagram. We propose to conceptualize the impact of a changing environment on the organization structure of a MAS in that manner.
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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 | 0.002 | 0.002 |
| 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 | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".