Application of Edge Computing in Real Time Data Processing to Enhance Non-Player Character Behavior in Game AI Systems

Mobile Edge Computing (MEC) frameworks improve real-time data processing and system scalability by making networked game AI NPCs more responsive and flexible. MEC-based frameworks are tested for latency reduction and NPC real-time performance in complex and dynamic environments. Simulated and real-life user experiments evaluated the proposed system's response times, accuracy, and latency. Python simulations of network settings with different NPC concentrations and complexity produced massive datasets. NPC behaviour feedback was collected from 100 diverse users of various ages, genders, gaming experiences, and preferences. In low- to medium-density scenarios, the edge computing framework improved NPC responsiveness with low latency and high accuracy, enhancing player immersion. Due to the environment's complexity and NPC density, response times increased and accuracy decreased, requiring further optimisation for harsher conditions. Despite bugs and repetitive behaviours that suggested the Likert scale could be improved, the qualitative results praised the NPCs' lively conversation and realistic movements. Edge computing improves game AI and NPC realism with adaptive responses and real-time data processing. Scaling NPC densities and integrating edge computing with game architectures require more research. Next, improve NPC AI algorithms, reduce computational complexity and scalability, and expand testing environment game scenarios. Edge computing and AI techniques like deep learning and natural language processing can create immersive and engaging gaming experiences. This may present new gaming industry challenges and opportunities for innovation. Edge computing's real-time data processing and adaptive responses may change video game non-player characters.