Swarm Intelligence in Multiplexed Robots Problems Identified
Bibliographic record
Abstract
Although kindling with the very consciousness of the human mind may have been banned, this did not stop scientists from tinkering and innovating with all the technology they were surrounded by. Stumbled by mathematical problems, mathematicians developed certain tactics we call algorithms. Algorithms are the layman's logic language for any device to function according to the desired task.Well, Mother Nature has already built us an unimaginable and incomprehensible algorithm propelled by personal choices, emotions, feelings, and sometimes rationality. For people who still didn't get it, we call it brain in day-to-day language. Our mind subconsciously designs such algorithms for us to efficiently function in our every day to day lives. Our project is an effort to reconstruct such rationality of thinking by artificial means through the use of reinforcement learning and robots to conclude the following 1) Is it possible to recreate human understanding? 2) If yes, then how do we channel it into something practical and physical? 3) How do we utilize this so-formed machine as a result of the conclusion of the second question? Asking this to ourselves we pursued to make our project P3 Hexa which is an endeavor to tinker with swarm intelligence and physically experience the functioning of a self-designed algorithm
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.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".