Detection and verification of a new type of emergent behavior in multiagent systems
Bibliographic record
Abstract
The verification of Distributed Software Systems (DSS) and Multi agent systems (MAS) has taken a special attention due to the growing demand of having DSS in this decade. MAS and DSS are a class of software in which functionality or control is distributed. This may cause components (agents) to emerge an unexpected behavior in their runtime, which was not seen in the requirement and design. This is known as emergent behavior of components. The cost of detecting and fixing of such problem is much more valuable compared to fix them after deployment. Therefore, in this paper a new type of emergent behavior that can happen in MAS is investigated. A method for verification of this type of emergent behavior is proposed followed by an algorithm. This type of emergent behavior can not be detected with the existing methods of emergent behavior detection. This type of emergent behavior focuses on one component when it misses the information about the senders of the same message from different components. The contribution of this work rather than investigating this type of emergent behaviors is on its verification method and also proposing a solution to fix it. The details are shown through a case study on MaSE artifacts which is an Agent Oriented Software Engineering 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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".