Local Agentic Theory for Accessible Mobile Games

Traditional game difficulty systems use rigid rules that fail to adapt to diverse player abilities. Cloud-based alternatives solve this adaptability problem, but they introduce high network latency and significant server expenses. Local Agentic Theory solves these issues by running small, autonomous reinforcement learning models directly on edge devices using optimized runtimes like LiteRT. Operating locally at frame rates up to 60 Hz cuts processing latency to around 3 milliseconds while keeping user sensory data private. By balancing memory, processing time, and thermal limits through search algorithms like Monte Carlo Tree Search, the framework adjusts game difficulty dynamically without overloading device resources.