Affinity Classification Problem by Stochastic Cellular Automata

TL;DR

This paper introduces the affinity classification problem, a generalization of the density classification problem, and proposes a stochastic cellular automata model with applications in self-healing systems.

Contribution

It presents a novel affinity classification problem and a stochastic cellular automata framework to address it, expanding the scope of cellular automata applications.

Findings

Model can be applied to self-healing systems

Stochastic rules improve classification capabilities

Framework generalizes density classification problem

Abstract

This work introduces a new problem, named as, affinity classification problem which is a generalization of the density classification problem. To solve this problem, we introduce temporally stochastic cellular automata where two rules are stochastically applied in each step on all cells of the automata. Our model is defined on 2-dimensional grid having affection capability. We show that this model can be used in several applications like modeling self-healing systems.