Research on the Impact of Multi-Agent Based Systems on the Development of Intelligent Manufacturing
Bibliographic record
Abstract
Agent technology is widely used in intelligent manufacturing and digital workshop as a new method to solve complex, dynamic and distributed arti icial intelligence application problems.This paper irstly summarizes the application steps of Agent technology in 3 aspects of modeling, simulation and monitoring of intelligent manufacturing system on the basis of a brief description of multi-agent system.Then, based on reinforcement learning theory, a multi-agent collaborative algorithm SRL_M3DDPG based on state representation learning is proposed.Finally, the algorithm model is tested and applied to the smart shop scheduling problem.The learning curve of the SRL_M3DDPG algorithm in the example remains relatively stable after the 3400th round, and the maximum completion time of the scheduling is 29.Comparing with other composite scheduling rules, the delay rate of this paper's algorithmic model is the lowest, which is only 15.47%, which indicates that the algorithm is able to signi icantly reduce the delay rate of the workpiece.In addition, this paper's algorithm achieves better results in adaptive intelligent manufacturing workshop scheduling, inding the shortest machining completion time of 221 unit time, which can adapt to the dynamic intelligent manufacturing workshop environment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".