Fault Tree Analysis And Risk Mitigation Strategies For Autonomous Systems Via Statistical Model Checking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In order to assess the reliability of autonomous systems, fault tree analysis (FTA) technique is used extensively. Most of the traditional FTA approaches are based on simulation and often require extensive computing capabilities. This paper proposes a formal FTA approach that can investigate the probability of failure of autonomous systems. The proposed methodology takes advantage of both FTA and statistical model checking (SMC). In order to illustrate the proposed approach, the sources of communication failure in a fleet of UAVs are analyzed. After detecting the most critical causes of communication failure, several redundant architectures are examined to assess their potentials to mitigate the risks of system failure. The results illustrate that all of the investigated architectures are capable of mitigating the probability of failure of the fleet of UAVs under studies.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it