Cooperative Decision-Making in Decentralized Multiple-Robot Systems: The Best-of-N Problem
Bibliographic record
Abstract
Multiple-robot systems (MRS) that are decentrally organized have many benefits over centralized systems. Decentralized systems are less affected by computational and communicative bottlenecks, and they are more robust to the loss of individual member robots. System-level cognitive operations, though, are much more difficult to implement in decentralized systems. One example is the best-of-N decision-making problem, in which a team attempts to unanimously select a single alternative from a list that maximizes a given metric. This is a valuable operation, since many system-level operations can be expressed in this form. Optimal best-of-N decision-making, however, is intractable in large decentralized systems. The contribution of this paper is a biologically inspired algorithm that enables a decentralized MRS composed of very simple robots to make good, unanimous decisions. In a series of physical experiments using real robots, the best decision was made at least 80% of the time. In all, 100% of the decisions achieved perfect consensus, which prevented the MRS from becoming fragmented. The decisions are made using anonymous, local communication, with no direct comparisons of the available alternatives by the individual robots.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".