On Enhancing Recent Multi-player Game Playing Strategies Using a Spectrum of Adaptive Data Structures
Bibliographic record
Abstract
Multi-Player Game Playing (MPGP) strategies have predominantly been built on the basis of utilizing Two-Player Game Playing (TPGP) strategies that were designed for games such as Chess and Go. However, a few strategies, such as the Best-Reply Search (BRS), that have been specifically tuned for the multi-player setting, have been introduced in the literature. Recently, these strategies have been further optimized by incorporating into them techniques from the field of Adaptive Data Structures (ADS) [1]. In this paper, we extend this area of research by demonstrating the efficacy of a broader spectrum of techniques from the field of ADS. The results presented in [1] have been enhanced in two directions, namely by considering a set of list-based ADSs capable of "ranking" the relative strengths of the perspective player's opponents, and by also considering the ply-depth to which the ADSs can be invoked. The results that we present conclusively prove that the incorporation of ADSs positively enhances the BRS, that the semantics of the ADS scheme used question can influence its performance, and that the advantage gleaned remains at deeper search depths.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".