An Intelligent Agent for Testing Distributed Agent-based Systems
Bibliographic record
Abstract
Often a Catch-22 situation arises when developers start to test agent-based and other distributed systems. They can not test their agents until other agents are available. Developers need other agents in a community to send messages to their agents to trigger events. Therefore, a sizeable set of agents, none of which may have been developed or tested, may be needed to test a single agent. To get around this problem, developers write dummy agents that do not take any input and just send out predefined messages. This creates extra work and time for the developer and if requirements change these agents will need to be updated. For agent-based systems that learn, a large battery of testing is needed to test that the learning performs correctly. The test and monitoring agent described in this paper is an intelligent test agent that has automatic and semi-automatic features that allows complete testing to be done in less time and provide testing of the system in parts or as a whole. This will provide developers and testers a single system to do both testing and monitoring and will also reduce the overhead in learning multiple systems and provide savings through the reduced number of tools to learn and maintain.
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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 | 0.000 | 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 | 0.000 | 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".