Bibliographic record
Abstract
Abstract. Game-SAT is a 2-player version of SAT where two players (MAX and MIN) play on a SAT instance by alternatively selecting a variable and assigning it a value true or false. MAX tries to make the formula true, while MIN tries to make it false. The Game-SAT problem is to determine the winner of a SAT instance under the rules above, assuming the perfect play by both players. The Game-SAT problem, originally derived from an application in adversarial planning, is PSPACE-complete. The problem is similar to QBF, but differs by the property of free variable selection, compared to QBF with its fixed variable ordering. We have developed a Game-SAT solver, Gasaso, that uses a combination of standard game tree search techniques, search methods that are well known in the SAT community, and specialized pruning techniques. We show empirically how the solver performs in this new domain, and give evidence for the existence of phase transitions in this problem. Keywords: Game-SAT, heuristic search, empirical study 1
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.
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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.059 | 0.015 |
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".