DIMMACSS-Stage: a Distributed Intelligence Model for a Multi-Agent Control System using Simulink and the Stage robotic simulator
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
DIMMACSS-Stage is a software framework used to control a multi-agent system within the Player/Stage robotic simulator using a controller designed in MATLAB’s Simulink. There is a lot of novel research being done that involves the control of a multi-agent system, and a lot of this research uses robotic simulators, such as Player/Stage, to test and prove the validity of the controller. However, the current trend throughout the published research, even in the top journals and conferences, is that the software framework is constantly being designed from scratch over and over again, and there is little to no discussion regarding how it was actually designed. There are many problems associated with this situation; for example, the solutions are most likely not optimal in their time and space complexities, and there is no way to duplicate or replicate the experimental results of the published work. To this end, this paper provides a method, model, and design regarding how to specifically design a correct and optimal software framework to control a multi-agent system in the Stage robotic simulator using a controller designed in MATLAB’s Simulink. This paper also discusses why optimal solutions are important, and it provides all the information and details necessary to replicate and duplicate the DIMMACSS-Stage framework.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it