MATLAB Implementation of Cellular Automata for Knowledge Propagation Simulation

Below is the MATLAB source code implementation for the cellular automata program. The state evolution rules of the cellular automata are programmed as follows: 1. For a cell in state 0, when it has neighboring cells in state 1, its state transitions to state 1 with probability a×b. The transition probability increases proportionally with the number of state-1 neighbors in its Moore neighborhood (implemented using neighbor counting and probability scaling functions). 2. A cell in state 1 maintains its state unchanged throughout the simulation (achieved through conditional state preservation in the update loop). To effectively demonstrate knowledge propagation patterns, we initialize the simulation with a 50×50 grid where the initial propagator is positioned at the center cell (25×25). The implementation uses Moore neighborhood configuration (all 8 surrounding cells) and runs for 50 simulation time steps to generate the knowledge propagation visualization. The MATLAB code structure includes grid initialization using zeros matrices, neighbor detection through boundary-handled indexing, probability calculations using random number generation (rand function), and sequential state updates through nested loops. The simulation outputs a animation showing progressive propagation patterns. (Insert simulation visualization here) The results demonstrate successful knowledge diffusion from the center outward over time, validating that our cellular automata implementation effectively models knowledge propagation dynamics. The color-coded visualization shows increasing coverage areas corresponding to propagation probability parameters and neighborhood interactions.