AURA - Adaptive User-Responsive Architecture

Abstract

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Dynamic Difficulty Adjustment (DDA) systems attempt to keep players engaged by varying the difficulty of the game in response to observed behaviour. Most current methods initiate adaptation immediately, resulting in cold-start bias, erratic early behaviour, and reduced interpretability. To overcome these limitations while maintaining player agency and behavioural validity, this paper presents a calibration-first, telemetry-driven adaptive game framework. Before any intervention, the system creates a statistically neutral behavioural reference by clearly separating the initial calibration from real-time adaptation. Soft membership across combat, exploration, and collection archetypes is used to model player behaviour while capturing behavioural fluidity without strict categorisation. An explainable ANFIS-inspired neuro-fuzzy reasoning formulation processes these representations with temporal deltas to generate bounded, forward-looking difficulty adjustments. Stable adaptation is ensured and oscillatory feedback is avoided through temporal smoothing and safety constraints. Empirical evaluation confirms the MLP surrogate approximates ANFIS-inspired teacher targets with 92.6% explained variance (R² = 0.9264), and that removing temporal behavioural signals collapses this fidelity by 38 percentage points, providing a principled foundation for reliable real-time adaptive gameplay.

Key Contributions