Improved Majority Identification by the Coarsened Majority Automaton

TL;DR

This paper introduces a coarsened automaton that improves majority identification in cellular automata, outperforming previous models while reducing computational costs, advancing understanding of local dynamics in automaton-based majority tasks.

Contribution

The paper presents a novel coarsened automaton that enhances majority identification performance and efficiency compared to existing automata.

Findings

Coarsened automaton outperforms its parent in majority identification.

Reduced computational costs with improved accuracy.

Advances understanding of local dynamics in cellular automata.

Abstract

The initial majority identification task is a fundamental test problem in cellular automaton research. To pass the test, an automaton must evolve to a uniform configuration consisting of the state that was in the majority for any initial configuration, employing only its internal, local dynamics. It is known that no two-state automaton can perform the majority task perfectly. Thus, it is a matter of continuing interest to identify and analyze new automata with improved majority identification capability. Here, we show that a coarsened version of one of the best majority identifiers can out-perform its parent automaton while simultaneously reducing the associated computational costs.