Dynamic Level of Difficulties Using Q-Learning and Fuzzy Logic

Maintaining player engagement in serious management games is a challenge due to the repetitive nature of traditional predetermined difficulty levels. A dynamic difficulty adjustment (DDA) system is introduced in this study to address this issue by integrating fuzzy logic and Q-learning. Player ennui is frequently the consequence of static difficulty adjustments. In order to dynamically adjust game complexity in accordance with player performance and preferences, our DDA system utilizes a diverse array of performance metrics, adaptive narrative elements, and real-time feedback, as well as fuzzy logic and Q-learning algorithms. According to empirical assessments, players were 28% more effective overall, and play sessions lasted an average of 35% longer. Player satisfaction and involvement were also much improved. Customers played the game longer and were less bored because of the higher degree of difficulty and customization. The integration of fuzzy logic and Q-learning in DDA systems greatly enhances the ability to maintain long-term player engagement in essential management games. This approach offers a long-lasting alternative for creating constantly captivating gaming experiences by effectively reducing the repetitiveness of traditional difficulty adjustments.