Adaptive Cellular Automata via the Fundamental Universal Learning Patterns (FULPs) Framework

This paper presents an initial self-contained and empirically validated implementation of the Fundamental Universal Learning Patterns (FULPs) framework within a cellular automata environment. While cellular automata are powerful models of emergent behavior, adaptive capacity within these systems is typically imposed through global mechanisms rather than generated through local interaction. The FULPs framework addresses this limitation by enabling cells to learn, adapt, and coordinate based solely on local state information. Across five iterative experimental versions, the framework demonstrated improvements in learning quality, perturbation recovery, confidence-gated stability, and spatial coordination. Final results showed significant improvements in recovery timestep over baseline conditions (Cohen’s d = -13.307, 50/50 runs), reduced post-perturbation instability (d = -1.96), and measurable spatial clustering in adaptive responses (Moran’s I, p = 0.028). These findings demonstrate that a lightweight, locally operable adaptive architecture can produce coordinated emergent resilience in a deterministic cellular automata system without backpropagation, global optimisation, or centralised control. This establishes an initial empirical basis for locally generated adaptive behavior within cellular automata through the FULPs framework.