Runtime Monitoring of Multi-agent Manufacturing Systems for Deadlock Detection Based on Models
Bibliographic record
Abstract
There is an increasing demand for the multi-agent systems (MAS) in the automation of manufacturing systems. However, similar to other distributed systems, autonomous agents' interaction in the automated manufacturing systems (AMS) can potentially lead to runtime behavioral failures including deadlock. Deadlocks can cause major financial consequences by negatively affecting the production cost and time. Therefore, a multi-agent manufacturing system should be monitored against the unwanted emergent behaviors such as deadlocks. In this paper, we propose a monitoring technique for deadlock detection in multi-agent manufacturing system based on the MAS design models. In this technique, the MAS is instrumented with a dedicated communication protocol to use the potential deadlock information derived from the design models to propagate deadlock detection query messages. The technique is able to reduce the message communication overhead among the agents by limiting the number of agents that the deadlock detection query messages should be initiated to.
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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".