Detection of implied scenarios in multiagent systems with clustering agents' communications
Bibliographic record
Abstract
Software agents in Multiagent Systems (MAS) have several interactions that are designed and represented in the scenarios of the system. These communications should be verified to detect whether the agents will show a new behavior in their execution, which is known as emergent behavior or implied scenario. Most research use different versions of state machines modeling for the detection of implied scenarios, which consider the states of one/all agents. The existing detection processes ignore the interactions among agents. In this paper, besides modeling the states and agents' behaviors, we model the agents' interactions derived from their designs, to detect implied scenarios. A new type of implied scenario that occurs when a process misses the information about its common communications in multiple scenarios is studied in this paper. This type of implied scenario cannot be detected with other approaches. Various situations that can lead to this implied scenario are ruled. Moreover, a detection methodology based on clustering the agents' communications from the scenarios of the system is presented. The results are verified through a case study.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".