Distributed game-tree search using transposition table driven work scheduling
Bibliographic record
Abstract
The /spl alpha//spl beta/ algorithm for two-player game-tree search has a notorious reputation as being a challenging algorithm for achieving reasonable parallel performance. MTD(f), a new /spl alpha//spl beta/ variant, has become the sequential algorithm of choice for practitioners. Unfortunately, MTD(f) inherits most of the parallel obstacles of /spl alpha//spl beta/, as well as creating new performance hurdles. Transposition-table-driven scheduling (TDS) is a new parallel search algorithm that has proven to be effective in the single-agent (one-player) domain. This paper presents TDSAB, the first time TDS parallelism has been applied to two-player search (the MTD(f) algorithm). Results show that TDSAB gives comparable speedups to that achieved by conventional parallel /spl alpha//spl beta/ algorithms. However, since this is a parallelization of a superior sequential algorithm the results in fact are better. This paper shows that the TDS idea can be extended to more challenging search domains.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".