Bibliographic record
Abstract
*† State identification is a crucial function in an autonomous system. The result of state identification is the basis for fault diagnosis and autonomous planning in an autonomous agent. NASA has developed Livingstone to perform state tracking, which is the kernel in their remote autonomous agent. The Remote Agent has been successfully tested in Deep Space One spacecraft. The theory behind Livingstone is based on General Diagnostic Engine (GDE) which aims to detect all possible states. On the other hand, Livingstone only tracks several most possible states. Based on this observation, we believe that GDE is computationally too intensive for state identification in spacecraft subsystem diagnosis. We have also observed that the number of sensors is relatively smaller than the number of components in a system. This motivated us to develop a symptom driven state-tracking algorithm which reduces the memory requirement and increases the execution speed. In this paper, we analyze simple examples and summarize characteristics of an autonomous system which can help simplify the diagnosis. Given a structural and behavioral model of a system, we can use techniques from set theory to diagnose faults. The diagnosis process is triggered by a discrepancy between the observation and the model prediction. Our method avoids the exponential computational problem faced by the traditional approaches such as GDE. Probability theory is used to support our approach and a command-based probing (configuration) technique is also discussed. A diagnosis system is implemented based on our technique and applied to a simple spacecraft propulsion subsystem diagnosis.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".