The Use of Deep Learning to Improve Player Engagement in a Video Game through a Dynamic Difficulty Adjustment Based on Skills Classification
| Autor: | Edwin A. Romero-Mendez, Pedro C. Santana-Mancilla, Miguel Garcia-Ruiz, Osval A. Montesinos-López, Luis E. Anido-Rifón |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Applied Sciences, Vol 13, Iss 14, p 8249 (2023) |
| Druh dokumentu: | article |
| ISSN: | 13148249 2076-3417 |
| DOI: | 10.3390/app13148249 |
| Popis: | The balance between game difficulty and player skill in the evolving landscape of the video game industry is a significant factor in player engagement. This study introduces a deep learning (DL) approach to enhance gameplay by dynamically adjusting game difficulty based on a player’s skill level. Our methodology aims to prevent player disengagement, which can occur if the game difficulty significantly exceeds or falls short of the player’s skill level. Our evaluation indicates that such dynamic adjustment leads to improved gameplay and increased player involvement, with 90% of the players reporting high game enjoyment and immersion levels. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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