Global Temporal Logic Control Synthesis for Multiagent Systems With Time and Space Margin
Bibliographic record
Abstract
This article considers the multiagent control problem under global temporal logic tasks, where agents possess heterogeneous capabilities. The global temporal logic task imposes requirements such as frequency and agent capabilities on the completion of signal temporal logic (STL) tasks, while also encompassing coupling relationships between agents. The complex formulation makes it challenging to achieve efficient task assignments with desired spatial-temporal control performance. To tackle this issue, a global language called extended capability temporal logic plus (ECaTL+) is first introduced for task formulation. The robustness measure for ECaTL+ is formally provided from both space and time perspectives. A hierarchical model predictive control (MPC) framework is then proposed to achieve task-satisfied motion planning with time and space margins, along with efficient computation. In the upper layer, a centralized MPC is formulated to allocate ECaTL+ tasks for time robustness maximization. Two mixed-integer linear programming (MILP) encoding methods are provided: one is complete, while the other employs formula simplification to save computation costs. Building upon the task assignment results, the lower layer focuses on realizing path planning for each agent by maximizing space robustness within the framework of iterative distributed MPC. Simulations and experiments are conducted to demonstrate the efficiency of the proposed algorithm.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".