| Autor: |
C. Brady, R. Gonen, G. Rabinovich |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 12, Pp 114086-114099 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3443873 |
| Popis: |
In game design, creators strive to guide players toward specific strategies through the game’s mechanics. This assumes a level of predictability in player behavior, though in reality, players often deviate from expected rational strategies. We provide an ensemble mechanism to influence behavior in arbitrary normal-form games, even when players are not rational. Our algorithm adjusts the game’s reward structure to encourage gameplay that aligns with the designer’s intentions. At the algorithm’s core is a deep reinforcement learning technique that learns to model players’ real-world behavior. This mechanism is versatile and applicable beyond game design; it can be employed in various fields where one can frame problems as classification tasks. Incorporating actual gameplay data into the training process allows our algorithm to acquire a practical understanding of player decisions. The efficacy of our mechanism is evaluated by testing the mechanism’s performance against a panel of classifiers, which includes a support vector machine, random forest, and multi-layer perceptron. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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