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 this dissertation, a state-based regression function for planning domains where an agent does not have complete information and may have sensing actions is presented. Both binary and multi-valued domains are considered, and the 0-approximation [SB01] is employed to define regression with respect to that semantics. In binary domains, the use of 0-approximation means using threevalued (true, false, and unknown) states. In multi-valued domains, each fluent in a state is assigned an unknown value or a value in a finite set of the fluent's prescribed values. Although planning using this approach is incomplete with regard to the full semantics, it is adopted to have a lower complexity. The soundness and completeness of the regression formulation with regard to the definition of progression are presented. More specifically, the dissertation shows that a plan obtained through regression for a planning problem is indeed a progression solution of that planning problem, and that for each plan found through progression, using regression one obtains that plan or an equivalent one. A contingent planner that utilizes the regression function is then developed and the soundness and completeness of the planning algorithm are proved. Heuristic measures are also employed to improve the planning performance. Experimental results with respect to several well-known planning problems in the literature and self-created domains are presented.
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 | 0.005 | 0.011 |
| 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 | 0.000 | 0.000 |
| Open science | 0.002 | 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