Emergence decision using hybrid rough sets/cellular automata

Abstract

Purpose

The aim is identifying and analyzing some well‐defined types of emergence where the paper uses ideas from machine learning and artificial intelligence to provide the model of cellular automata based on rough set theory and response in simulated cars.

Design/methodology/approach

This paper proposes, as practical part, a road traffic system based on two‐dimensional cellular automata combined with rough set theory to model the flow and jamming that is suitable to an urban environment.

Findings

The automaton mimics realistic traffic rules that apply in everyday experience.

Research limitations/implications

The modeled development process in this paper involves simulated processes of evolution, learning and self‐organization.

Practical implications

Recently, the examination and modeling of vehicular traffic has become an important subject of research.

Originality/value

The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of the state machine behavior, which can give an emergent to the model.

Citation

Hassan, Y. and Tazaki, E. (2006), "Emergence decision using hybrid rough sets/cellular automata", Kybernetes, Vol. 35 No. 6, pp. 797-813. https://doi.org/10.1108/03684920610662593