Abstracts of Recent PhDs
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.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.384 | 0.234 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".