Bibliographic record
Abstract
The design and analysis of strategies for playing strategic board games, is a core area of Artificial Intelligence (AI) that has been studied extensively since the inception of the field. However, while two-player board games are very well known, comparatively little research has been done on Multi-Player games, where the number of self-interested, competing players is greater than two. Furthermore, known strategies for multi-player games have difficulties performing on a level of sophistication comparable to their two-player counterparts. The premise of this thesis is the hypothesis that game playing in general, and the problem of multi-player games in particular, can benefit from efficient ranking mechanisms, for moves, board positions, or even players, in the multi-player scenario. The research done in this work confirms the hypothesis. Indeed, we have discovered that this information can be applied to improve game tree pruning, and within other possibilities. In this thesis, we observe that the formerly-unrelated field of Adaptive Data Structures (ADSs), which provide mechanisms by which a data structure can reorganize itself internally in response to queries, can provide a natural ranking mechanism. The primary motivation of this thesis is to demonstrate that the low-cost ADS-based data structures can provide this ranking mechanism to game playing engines, and furthermore generate statistically significant improvements to their efficiency. In this work, we will conclusively prove that ADS-based techniques are able to enhance existing multi-player game playing strategies, and perform competitively with state-of-the-art two-player techniques, as well. We demonstrate, through two general-use, domain independent move ordering heuristics, the Threat-ADS heuristic for multi-player games, and the History-ADS heuristic for both two-player and multi-player games, that ADSs are, indeed, capable of achieving this improvement. We present an examination of their performance in a very wide range of game models and configurations. We thus conclusively demonstrate that ADSs are able to achieve strong performance, in game playing engines, in the vast majority of cases. Our work in this thesis provides not only these domain-independent, formal move ordering heuristics, but furthermore serves as a strong example for future investigation into combinations between the fields of ADSs and game playing.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".