Player's Affective States as Meta AI Design on Augmented Reality Games - Andry Chowanda; Vincentius Dennis; Virya Dharmawan; Joseph Danielson Ramli (Society of Visual Informatics, 2023)
Games are considered one of the most popular entertainment forms worldwide. The interaction in the game environment makes the players addicted to playing the game. One technique to build an addicting game is utilizing the player's emotions using Meta Artificial Intelligence (AI). The player's emotions can be utilized by adjusting the game difficulty. Most of the game offers static and steady difficulty development throughout the game. This research proposes a Meta AI game design using the player's affective states. We argue that a dynamic difficulty development throughout the game will increase the player's game experiences. The player's facial expressions are utilized to extract the player's affective state information. To recognize the player's facial expressions, a Facial Expressions Recognition (FER) model was trained using VGG-16 architecture and The Indonesian Mixed Emotion Dataset (IMED) dataset in addition to a self-collected dataset. The emotions recognition model (from player's facial expressions) achieved the best validation accuracy of 99.98%. The model was implemented in the proposed Meta AI game design. The Meta AI game design proposed in this game was implemented in several game scenarios to be compared and evaluated. The proposed Meta AI game design was evaluated by 31 respondents using Game Experiences Questionnaire (GEQ). Overall, the results show that the game with Meta AI and Augmented Reality implemented significantly improved the Game Experiences Questionnaire (GEQ) score and the player's overall satisfaction compared to the other game scenarios.