Applying use case maps to multi-agent systems: a feature interaction example
Why this work is in the frame
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Bibliographic record
Abstract
Multi-agent systems are emerging as a potential solution to the problem of constructing flexible network-based software. A characteristic of such systems is that whole-system behaviour patterns emerge from the combination of many details in many agents, in sometimes intricate ways. Understanding the big picture by composing the details is often difficult and designing the details to achieve some desired whole-system behaviour pattern can easily become a cut-and-try exercise. To help solve these problems, the authors offer use case maps (UCMs) to provide a first-class representation of whole-system behaviour patterns, at a level above details. To illustrate the approach, they apply it to a classical distributed system problem of a kind that agent systems must be capable of solving, namely feature interaction in telephony.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it