Multi Agent-Aided Tools for Engineering System Design: A Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
SummaryThis paper introduces key features of a multi-agent prototype, which is integrated in a well-known approach of development of distributed artificial intelligence tools, and constitutes the first stage of our effort towards a multi-agent system for designing engineering applications and especially power electronic converters. The design needs a high level of expertise and requires the cooperation of several multidisciplinary groups. In order to ensure the coordination of the design activities of these groups. we associate each expert that participates in designing the power circuit with an agent, which substitutes this expert and will simulate his reasoning. To manage Communications between agents. we hare chosen a Java implementation of the Actor model, called Épidaure, which constitutes an environment where communications are done Via message passing. This paper will give an outline of some artificial intelligence applications. Emerging theories in the area are also reviewed. The paper will present more particularly the aspect of the agent structure and reasoning process, and will show how a decentralized organization with simple communication is a reasonable trade-off between a centralized architecture and the use of global knowledge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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 it