Bibliographic record
Abstract
Retrograde analysis has been applied to many problems. It enables to generate databases of positions or databases of patterns. For each possible position or pattern it enables to find the status of the position and other information such as the minimal number of moves required to win in the position. Once generated, databases enable to control, reduce or even replace search. Retrograde analysis was first used to solve chess endgames [van den Herik and Herschberg 1985; Thompson 1986; Stiller 1996; Thompson 1996] containing up to six pieces. Chess endgame databases enable to play endgames perfectly and even discovered new chess knowledge about endgames [Nunn 1993]. Another successful application of retrograde analysis is the computation of Checkers endgames by Chinook [Lake et al. 1994; Schaeffer 1997] which is an important part of the program that solved Checkers [Schaeffer 2007]. Retrograde analysis has also been used in single player games such as the 16 puzzle. It consisted in computing an admissible heuristics involving only some of the pieces [Culberson and Schaeffer 1998]. Pattern database can also be combined and improve on single pattern databases [Korf and Felner 2002]. Another application of pattern databases is Rubik’s cube [Korf 1997] where separate databases for corner and side cubes can be computed and improve much the admissible heuristic. Pattern databases can also be used for the game of Go, computing for example databases on eyes or on life [Cazenave 1993; Cazenave 1996b; Cazenave 1996a]. Improvements include associating patterns to abstract conditions such as external liberties [Cazenave 2001] and reducing memory requirements using metarules [Cazenave 2003]. Some complex games such as Awari have been completely solved with retrograde analysis [Romein and Bal 2003]. In his thesis [Fraser 2002], Bill Fraser describes the BruteForce program that searches an endgame region in Go to calculate thermographs for every position. It enables his program to find means, temperatures, and orthodox lines of play. Our work is related since we use a brute force approach that takes ko into account, however we simply compute the values of positions and not the associated thermograph. Moreover we deal with long loops in the game graph, long loops only very rarely occur in Go positions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".