Multi-agent modelling of cyber-physical systems for IEC 61499-based distributed intelligent automation
Bibliographic record
Abstract
Traditional industrial automation systems developed under centralized architectures are statically programmed with determined procedures to perform predefined tasks in structured environments. The major challenges for these legacy systems are that they are unable to automatically discover alternative solutions, flexibly coordinate reconfigurable modules and actively deploy corresponding functions, to quickly respond to frequent changes and intelligently adapt to evolving requirements in dynamic environments. This paper presents a two-layer architecture modelling framework, including the high-level cyber module designed as multi-agent computing model and the low-level physical module designed as agent-embedded IEC 61499 function block model, to enable real-time adaptation at the device level and run-time intelligence throughout the whole system. The design results in a new computing module for high-level multi-agent-based automation architectures and a new design pattern for low-level function block modelled control solutions. The design is demonstrated and evaluated through various tests on the multi-agent simulation model developed in NetLogo and the experimental testbed designed on the Jetson Nano and Raspberry Pi platforms. The result shows that the design is feasible with improved performances and expected capabilities to respond to major challenges in Industry 4.0.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".