Two-dimensional traffic rules and the density classification problem

TL;DR

This paper explores solutions to the two-dimensional density classification problem, proposing new models using interacting particle systems and evaluating their performance through simulations.

Contribution

It introduces exact and randomized solutions for the 2D density classification problem using interacting particle systems, extending prior 1D approaches.

Findings

Particle spacing problem has no cellular automaton solution in 2D.

Proposed models perform well in simulations.

New methods extend 1D traffic rule concepts to 2D.

Abstract

The density classification problem is the computational problem of finding the majority in a given array of votes in a distributed fashion. It is known that no cellular automaton rule with binary alphabet can solve the density classification problem. On the other hand, it was shown that a probabilistic mixture of the traffic rule and the majority rule solves the one-dimensional problem correctly with a probability arbitrarily close to one. We investigate the possibility of a similar approach in two dimensions. We show that in two dimensions, the particle spacing problem, which is solved in one dimension by the traffic rule, has no cellular automaton solution. However, we propose exact and randomized solutions via interacting particle systems. We assess the performance of our models using numeric simulations.