Structural conrollability of multi-agent systems subject to partial failure
Bibliographic record
Abstract
Formation control of multi-agent systems has emerged as a topic of major interest during the last decade, and has been studied from various perspectives using different approaches. This work considers the structural controllability of multi-agent systems with leader-follower architecture. To this end, graphical conditions are first obtained based on the information flow graph of the system. Then, the notions of p -link, q- agent, and joint-(p, q ) controllability are introduced as quantitative measures for the controllability of the system subject to failure in communication links or/and agents. Necessary and sufficient conditions for the system to remain structurally controllable in the case of the failure of some of the communication links or/and loss of some agents are derived in terms of the topology of the information flow graph. Moreover, a polynomial-time algorithm for determining the maximum number of failed communication links under which the system remains structurally controllable is presented. The proposed algorithm is analogously extended to the case of loss of agents, using the node-duplication technique. The above results are subsequently extended to the multiple-leader case, i.e., when more than one agent can act as the leader. Then, leader localization problem is investigated, where it is desired to achieve p -link or q -agent controllability in a multi-agent system. This problem is concerned with finding a minimal set of agents whose selection as leaders results in a p -link or q -agent controllable system. Polynomial-time algorithms to find such minimal sets for both undirected and directed information flow graphs are presented
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".