Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,003 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».