Bibliographic record
Abstract
Fundamental aspects of decentralized systems are considered from a control perspective. The stochastic framework afforded by Markov systems is presented as a formal setting in which to study decentralized systems. A stochastic algebra is introduced which allows Markov systems to be considered in matrix format but also strikes an important connection to the classic linear system originally studied by Kalman [1960]. The process of decentralization is shown to impose constraints on observability and controllability of a system. However, it is argued that communicating decentralized controllers can implement any control law possible with a centralized controller. Communication is shown to serve a dual role, both enabling sensor data to be shared and actions to be coordinated. The viabilities of these two types of communication are tested on a real network of mobile robots where they are found to be successful at a variety of tasks. Action coordination is reframed as a decentralized decision making process whereupon stochastic cellular automata (SCA) are introduced as a model. Through studies of SCA it is found that coordination in a group of arbitrarily and sparsely connected agents is possible using simple rules. The resulting stochastic mechanism may be immediately used as a practical decentralized decision making tool (it is tested on a group of mobile robots) but, it furthermore provides insight into the general features of self-organizing systems.
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 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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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; both teacher heads agree on what is shown here.
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".