Characterization of the optimal disturbance attenuation for nonlinear stochastic partially observable uncertain systems
Bibliographic record
Abstract
This paper is concerned with stochastic optimal control systems, in which uncertainty is described by a relative entropy constraint between the nominal measure and the uncertain measure, while the pay-off is a functional of the uncertain measure. This is a minimax game, equivalent to the H/sup /spl infin// optimal disturbance attenuation problem, in which the controller seeks to minimize the pay-off, while the disturbance described by a set of measures aims at maximizing the pay-off. The objective of this paper is to apply the results of the abstract formulation to stochastic uncertain systems, in which the nominal and uncertain systems are described by conditional distributions. The results obtained include existence of the optimal control policy, explicit computation of the worst case conditional measure, and characterization of the optimal disturbance attenuation, for nonlinear partially observable systems. The linear case is presented to illustrate the concepts.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".