On the Computational Power of Mobile Robots Under Sequential Schedulers
Bibliographic record
Abstract
We consider distributed systems of autonomous, punctiform, mobile robots that operate in the Euclidean plane by executing an infinite sequence of Look-Compute-Move cycles. Robots are anonymous, indistinguishable, homogeneous, and disoriented. In literature, four base models have been proposed to study four different memory-communication settings: $$\mathcal {OBLOT}$$ (oblivious and silent), $$\mathcal {FSTA}$$ (finite-state and silent), $$\mathcal {FCOM}$$ (oblivious and finite-communication), and $$\mathcal {LUMI}$$ (finite-state and finite-communication). In particular, the research has investigated how the computational power of these models is affected by considering three main classes of robot schedulers: FSYNCH (fully synchronous), SSYNCH (semi-synchronous), and ASYNCH (asynchronous). This paper focuses on a peculiar type of SSYNCH schedulers, the sequential ones, which activate only one robot at each round. We consider three subclasses: the general sequential scheduler (SEQ), the permutation scheduler (PERM), and the well-known round-robin (RROBIN). For each base model, we investigate how the robots’ computational power changes as the scheduler class varies, thus providing a first overview of the computational landscape of sequential schedulers.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".