Local Agentic Theory for Accessible Mobile Games

Traditional game difficulty adjustment mechanisms rely on rigid, centralized, or predefined human-engineered heuristics that inadequately map to the heterogeneous landscape of human cognitive and motor accessibility. This paper introduces Local Agentic Theory, an architectural paradigm that deploys small, autonomous reinforcement learning and reasoning agents natively on edge devices to achieve dynamic, individualized game balancing. Moving beyond traditional cloud-bound models that introduce severe network latency (approximately 200 ms) and prohibitive server costs, our proposed framework operates strictly on-device via localized execution pipelines (approximately 3 ms latency) using optimized runtime environments like LiteRT.

By utilizing advances in sample-efficient reinforcement learning (e.g., EfficientZero architectures), these on-device agents decouple from continuous weight-optimization grinds, functioning instead through real-time "perceive-predict-decide-act" inference loops executed 60 times per second. We evaluate the efficacy of these local agents in balancing a real-time arcade shooter under rigid, multi-objective constraints, explicitly optimization across three localized scarcity budgets: memory capacity (RAM footprint), temporal windows (16 ms per frame), and thermal/battery endurance.

To govern this constraint satisfaction problem, we implement anytime Monte Carlo Tree Search (MCTS), Lagrangian penalty systems, and min-conflicts local search optimizations that enable the system to gracefully degrade auxiliary computational fidelity without breaching hard device limits. Furthermore, we demonstrate how integrating local sensory telemetry—such as on-device, uncalibrated eye-gaze region estimation—creates a continuous, privacy-preserving feedback loop that transforms intent recognition into passive accessibility assistance. Ultimately, this work offers a scalable, robust blueprint for shifting game accessibility from binary, cloud-dependent toggles to fluid, edge-computed experiential curation.