An on-line decision-theoretic Golog interpreter
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
We consider an on-line decision-theoretic interpreter and incremental execution of Golog programs. We introduce two new search control operators and demonstrate in an example how one of them can be used to save computational efforts. In addition to sensing actions designed to identify outcomes of stochastic actions, we consider a new representation for sensing actions that may return both binary and real valued data at the run time. Programmers may use sensing actions explicitly in Golog programs whenever results of sensing are required to evaluate tests. The representation for sensing actions that we introduce allows the use of regression, a computationally efficient mechanism for evaluation of tests. We describe an implementation of the on-line incremental decision-theoretic Golog interpreter in Prolog. The implementation was tested on the B21 robot Golem manufactured by RWI. 1 Introduction This report provides an overview of our recent research devoted to designing c...
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it