Bibliographic record
Abstract
Heuristic search algorithms (eg. A* and IDA*) with accurate lower bounds can solve impressively large problems optimally. Most lower bounds, such as the well known Manhattan Distance heuristic for the sliding-tile puzzles or the Assignment Problem lower bound for the Asymmetric Traveling Salesman problem, are the products of human ingenuity and insight. An alternative approach to obtain lower bounds is to precalculate shortest distances in an abstraction of the original search space which is derived automatically and store the bounds in pattern databases (look-up tables). This latter technique, based on the ideas of Culberson and Schaeffer, gained popularity when Korf for the first time solved random instances of Rubik's Cube using pattern databases. While researchers were pushing for solving larger and larger problems, the fact that there exist a very large number of abstract spaces that can provide lower bounds was overlooked. This thesis fills this gap in research by investigating the search performance of lower bounds derived from abstractions. We also use the results of this analysis to automatically derive high performance pattern databases. First, we establish a very predictable trade-off between search speed and the number of entries in the pattern database. Second, we derive simple statistics that can predict the search performance of pattern databases without performing actual searches in the original state space. Using these results, we derive high performance pattern databases to search for macro-operators and to solve challenging instances of the well known Sequential Ordering Problem (SOP). Macro-search is a good candidate to showcase automatically derived lower bounds since there are many search spaces and each needs a different lower bound. The SOP is an NP-hard optimization problem. We were able to solve an unsolved instance from the TSPLIB. This required a greedy search in the space of abstractions to find a sufficiently accurate lower bound and several novel enhancements to the basic branch and bound algorithm.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".