Modeling a fault tolerant multiagent system for the control of a mobile robot using MaSE methodology
Bibliographic record
Abstract
A multiagent system that tolerates failure in a hardware and software level in the distributed control system of a mobile robot is shown; it's made to guarantee the availability of the robot in the most efficient way. The multiagent system is modeled thru the formal MaSE methodology supported by its development tool, AgentTool; in such a way that a greater reliability is guaranteed. The multiagent system tolerates system failures in the robot's control systems through three types of agents that cooperate so that the mechanisms that detect failures in the input, output, processing and network control devices are activated; as well as the tasks that constitute the robot's control system, these agents also activate the failure-isolation mechanisms and reconfigure the system by means of interactions between the agents that are supported in the design of the physical architecture of the robot's control system, In our system, the agents are designed such that if they recover from the failure, the agents reconfigure the control system to the state prior to the failure, if they are not able to recover the failure, the robot's control system continues working due to the double connection and to the duplicity of the tasks and devices, because the implemented design in the physical architecture of the system allows it.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".